Intent-based orchestration using network parsimony trees

ABSTRACT

Novel tools and techniques are provided for implementing intent-based orchestration using network parsimony trees. In various embodiments, in response to receiving a request for network services that comprises desired characteristics and performance parameters for the requested network services without information regarding specific hardware, hardware type, location, or network, a computing system might generate a request-based parsimony tree based on the desired characteristics and performance parameters. The computing system might access, from a datastore, a plurality of network-based parsimony trees that are each generated based on measured network metrics, might compare the request-based parsimony tree with each of one or more network-based parsimony trees to determine a fitness score for each network-based parsimony tree, and might identify a best-fit network-based parsimony tree based on the fitness scores. The computing system might identify and might allocate network resources based on the identified best-fit network-based parsimony tree, for providing the requested network services.

COPYRIGHT STATEMENT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD

The present disclosure relates, in general, to methods, systems, andapparatuses for implementing network services orchestration, and, moreparticularly, to methods, systems, and apparatuses for implementingintent-based multi-tiered orchestration and automation and/orimplementing intent-based orchestration using network parsimony trees.

BACKGROUND

In typical network resource allocation schemes, a customer might providea request for network services from a set list of network services,which might include, among other things, information regarding one ormore of specific hardware, specific hardware type, specific location,and/or specific network for providing network services, or the like. Thecustomer might select the particular hardware, hardware type, location,and/or network based on stated or estimated performance metrics forthese components or generic versions of these components, but might notconvey the customer's specific desired performance parameters. Theservice provider then allocates network resources based on the selectedone or more of specific hardware, specific hardware type, specificlocation, or specific network for providing network services, asindicated in the request.

Such specific requests, however, do not necessarily provide the serviceprovider with the intent or expectations of the customer. Accordingly,the service provider will likely make network resource reallocationdecisions based on what is best for the network from the perspective ofthe service provider, but not necessarily what is best for the customer.Importantly, these conventional systems do not utilize metadata inresource inventory databases for implementing intent-based serviceconfiguration, service conformance, and/or service auditing.

Further certain networks do not provide for automated or automaticreallocation of network resources based on performance metrics of thenetwork and/or components or elements of the network. Accordingly, suchnetworks cannot automatically reallocate network resources based on bothperformance metrics of the network and/or components or elements of thenetwork and based on intent-based requests from a customer.

Hence, there is a need for more robust and scalable solutions forimplementing network services orchestration, and, more particularly, tomethods, systems, and apparatuses for implementing intent-basedmulti-tiered orchestration and automation and/or implementingintent-based orchestration using network parsimony trees.

BRIEF DESCRIPTION OF THE DRAWINGS

A further understanding of the nature and advantages of particularembodiments may be realized by reference to the remaining portions ofthe specification and the drawings, in which like reference numerals areused to refer to similar components. In some instances, a sub-label isassociated with a reference numeral to denote one of multiple similarcomponents. When reference is made to a reference numeral withoutspecification to an existing sub-label, it is intended to refer to allsuch multiple similar components.

FIG. 1 is a schematic diagram illustrating a system for implementingintent-based multi-tiered orchestration and automation and/orimplementing intent-based orchestration using network parsimony trees,in accordance with various embodiments.

FIGS. 2A and 2B are block diagrams illustrating various methods forimplementing intent-based multi-tiered orchestration and automation, inaccordance with various embodiments.

FIG. 3 is a schematic diagram illustrating another system forimplementing intent-based multi-tiered orchestration and automation, inaccordance with various embodiments.

FIGS. 4A-4D are flow diagrams illustrating a method for implementingintent-based multi-tiered orchestration and automation, in accordancewith various embodiments.

FIG. 5A-5I is a schematic diagram illustrating various implementationsfor intent-based orchestration using network parsimony trees, inaccordance with various embodiments.

FIGS. 6A-6E are flow diagrams illustrating a method for implementingintent-based orchestration using network parsimony trees, in accordancewith various embodiments.

FIG. 7 is a block diagram illustrating an exemplary computer or systemhardware architecture, in accordance with various embodiments.

FIG. 8 is a block diagram illustrating a networked system of computers,computing systems, or system hardware architecture, which can be used inaccordance with various embodiments.

DETAILED DESCRIPTION OF CERTAIN EMBODIMENTS

Overview

Various embodiments provide tools and techniques for implementingnetwork services orchestration, and, more particularly, to methods,systems, and apparatuses for implementing intent-based multi-tieredorchestration and automation and/or implementing intent-basedorchestration using network parsimony trees.

In various embodiments, a macro orchestrator might receive, over anetwork, a request for network services from a user device associatedwith a customer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services. The macro orchestrator mightsend, to a first micro orchestrator among a plurality of microorchestrators, the received request for network services, where themacro orchestrator automates, manages, or controls each of the pluralityof micro orchestrators, while each micro orchestrator automates,manages, or controls at least one of a plurality of domain managers or aplurality of network resources.

In response to receiving the request for network services, the firstmicro orchestrator might identify one or more first network resourcesamong a first plurality of network resources for providing the requestednetwork services, based at least in part on the desired characteristicsand performance parameters, and based at least in part on adetermination that the one or more network resources are capable ofproviding network services having the desired characteristics andperformance parameters. The first micro orchestrator might allocate atleast one first network resource among the identified one or more firstnetwork resources for providing the requested network services.

In some embodiments, the first micro orchestrator might (continually,occasionally, randomly, or in response to a request for data, or thelike) receive, from one or more first domain managers among a firstplurality of domain managers in communication with the first microorchestrator, data regarding the first plurality of network resourcesthat are automated, managed, or controlled by each of the one or morefirst domain managers. In such cases, identifying, with the first microorchestrator, one or more first network resources among a firstplurality of network resources for providing the requested networkservices might comprise identifying, with the first micro orchestrator,one or more first network resources among a first plurality of networkresources for providing the requested network services, based at leastin part on the data regarding the one or more first network resources,based at least in part on the desired characteristics and performanceparameters, and based at least in part on a determination that the oneor more network resources are capable of providing network serviceshaving the desired characteristics and performance parameters.

According to some embodiments, allocating, with the first microorchestrator, at least one first network resource among the identifiedone or more first network resources for providing the requested networkservices might comprise: sending, with the first micro orchestrator,commands to at least one first domain manager among the one or morefirst domain managers that automate, manage, or control the at least onefirst network resource; and in response to receiving the commands fromthe first micro orchestrator: determining, with the at least one firstdomain manager, an intent based at least in part on the desiredcharacteristics and performance parameters as comprised in the requestfor network services; generating and sending, with the at least onefirst domain manager, device language instructions for allocating the atleast one first network resource; and implementing, with the at leastone first domain manager, the at least one first network resource on theuser device associated with the customer, to provide the requestednetwork services.

In some embodiments, one of the macro orchestrator or the first microorchestrator might update a resource database with informationindicating that the at least one first network resource has beenallocated for providing the requested network services and withinformation indicative of the desired characteristics and performanceparameters as comprised in the request for network services. In somecases, an audit engine might determine whether each of the identifiedone or more first network resources conforms with the desiredcharacteristics and performance parameters. In some instances,determining whether each of the identified one or more first networkresources conforms with the desired characteristics and performanceparameters might comprise determining, with the audit engine, whethereach of the identified one or more first network resources conforms withthe desired characteristics and performance parameters on a periodicbasis or in response to a request to perform an audit. Alternatively, oradditionally, determining whether each of the identified one or morefirst network resources conforms with the desired characteristics andperformance parameters might comprise determining, with the auditengine, whether each of the identified one or more first networkresources conforms with the desired characteristics and performanceparameters, by: measuring one or more network performance metrics ofeach of the identified one or more first network resources; comparing,with the audit engine, the measured one or more network performancemetrics of each of the identified one or more first network resourceswith the desired performance parameters; determining characteristics ofeach of the identified one or more first network resources; andcomparing, with the audit engine, the determined characteristics of eachof the identified one or more first network resources with the desiredcharacteristics.

In such cases, each of the one or more network performance metrics mightinclude, without limitation, at least one of quality of service (“QoS”)measurement data, platform resource data and metrics, service usagedata, topology and reference data, historical network data, networkusage trend data, or one or more of information regarding at least oneof latency, jitter, bandwidth, packet loss, nodal connectivity, computeresources, storage resources, memory capacity, routing, operationssupport systems (“OSS”), or business support systems (“BSS”) orinformation regarding at least one of fault, configuration, accounting,performance, or security (“FCAPS”), and/or the like.

According to some embodiments, based on a determination that at leastone identified network resource among the identified one or more firstnetwork resources fails to conform with the desired performanceparameters within first predetermined thresholds or based on adetermination that the determined characteristics of the at least oneidentified network resource fails to conform with the desiredcharacteristics within second predetermined thresholds, the first microorchestrator either might reconfigure the at least one identifiednetwork resource to provide the desired characteristics and performanceparameters; or might reallocate at least one other identified networkresources among the identified one or more first network resources forproviding the requested network services.

In some aspects, each intent might be a goal for the service. These arenot policy related. Intents might typically be performance related, orservice component quantum oriented, which might mean delay, jitter,packet loss (performance), or service component (asset or path) types,geography, color, attribute, etc. might be considered. This means thatthe state engines in the service conformance (which might be on acontrol plane) must monitor and/or store those in local profiles for thebearer and/or service plane in order to make intent-based assignmentdecisions. These will require closed loop implementation, and the systemmight implement auditing to ensure that the state is trackedappropriately and that the network resources that are used forfulfilling requested network resources.

Importantly, the various systems utilize metadata in resource inventorydatabases (e.g., in resource inventory database, intent metadatadatabase, or an active inventory database, or the like) as well amulti-tiered orchestration system for implementing intent-basedmulti-tiered orchestration and automation, which may also provide forimplementation of intent-based services orchestration, as described ingreater detail in the '095, '244, and '884 Applications (which havealready been incorporated herein by reference in their entirety for allpurposes), implementation of intent-based service configuration, serviceconformance, and/or service auditing, as described in greater detail inthe '634 and '498 Applications (which have already been incorporatedherein by reference in their entirety for all purposes), and/orimplementation of disaggregated and distributed composableinfrastructure, as described in greater detail in the '308 Application(which has already been incorporated herein by reference in its entiretyfor all purposes).

In some aspects, one or more parsimony trees might be generated, basedon network telemetry data of one or more networks, where each parsimonytree might be a graphical representation of characteristics andperformance parameters based on the network telemetry data of the one ormore networks, and the system might perform network orchestration andautomation based on the generated one or more parsimony trees. Inparticular, a macro orchestrator and/or a computing system mightreceive, over a network, a request for network services from a userdevice associated with a customer, the request for network servicescomprising desired characteristics and performance parameters for therequested network services, without information regarding any ofspecific hardware, specific hardware type, specific location, orspecific network for providing the requested network services. The macroorchestrator and/or the computing system might send, to a first microorchestrator among a plurality of micro orchestrators, the receivedrequest for network services, where the macro orchestrator automates,manages, or controls each of the plurality of micro orchestrators, whileeach micro orchestrator automates, manages, or controls at least one ofa plurality of domain managers or a plurality of network resources. Inresponse to receiving the request for network services, the first microorchestrator and/or the computing system might generate a firstrequest-based parsimony tree based at least in part on the desiredcharacteristics and performance parameters contained in the request fornetwork services.

According to some embodiments, the first request-based parsimony treemight be a graphical representation including, without limitation, anend-point of a first portion representing delivery location of therequested network services, an endpoint of each of one or more secondportions that connect with the first portion representing a serviceprovider site, each intersection between two or more second portions orbetween the first portion and one of the second portions representing anetwork resource node, and characteristics of the first and secondportions representing the desired characteristics and performanceparameters contained in the request for network services, and/or thelike. In some cases, the plurality of micro orchestrators might eachinclude, but is not limited to, one of a server computer over a network,a cloud-based computing system over a network, or a distributedcomputing system, and/or the like.

The first micro orchestrator and/or the computing system might access,from a datastore, a plurality of first network-based parsimony trees,each of the plurality of first network-based parsimony trees beinggenerated based on measured network metrics. In some embodiments, eachfirst network-based parsimony tree might be a graphical representationincluding, but not limited to, an end-point of a third portionrepresenting the delivery location of the requested network services, anendpoint of each of one or more fourth portions that connect with thethird portion representing a service provider site, each intersectionbetween two or more fourth portions or between the third portion and oneof the fourth portions representing a network resource node, andcharacteristics of the third and fourth portions representing measuredcharacteristics and performance parameters based on the measured networkmetrics.

According to some embodiments, the first portion of the firstrequest-based parsimony tree and the third portion of each firstnetwork-based parsimony tree might each be represented by a trunk, whilethe one or more second portions of the first request-based parsimonytree and the one or more fourth portions of each first network-basedparsimony tree might each be represented by a branch, and, in eachparsimony tree, one or more branches might connect with the trunk. Insome cases, in each of at least one parsimony tree, two or more branchesmight connect with each other via one or more connector branches and viathe trunk, or the like. In some instances, each portion of each firstnetwork-based parsimony tree might be represented by a secondnetwork-based parsimony tree among a plurality of second network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion, where each portion of each secondnetwork-based parsimony tree might be represented by a thirdnetwork-based parsimony tree among a plurality of third network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion, and so on in a fractal-like manner.

In some embodiments, the characteristics of the first and secondportions and the characteristics of the third and fourth portions mightinclude, without limitation, at least one of thickness of each portion,length of each portion from the delivery location of the requestednetwork services, number of network resource nodes on each portion,color-code of each portion, number of second or fourth portions, angleof each second or fourth portion relative to the first or third portion,number of any connector portions between two or more second or fourthportions, relative location of any connector portions between two ormore second or fourth portions, length of any connector portions betweentwo or more second or fourth portions, or angle of any connectorportions between two or more second or fourth portions, and/or the like.In some instances, the characteristics of the first and second portionsand the characteristics of the third and fourth portions might representone or more of latency, jitter, packet loss, number of hops, bandwidth,utilization, capacity, or proximity, and/or the like.

According to some embodiments, the first micro orchestrator and/or thecomputing system might compare the first request-based parsimony treewith one or more first network-based parsimony trees among the pluralityof first network-based parsimony trees to determine a fitness score foreach first network-based parsimony tree. In some instances, each fitnessscore might be a value indicative of a level of heuristic matching (insome cases, embodied as a percentage match) between the firstrequest-based parsimony tree with one of the one or more firstnetwork-based parsimony trees. In some embodiments, comparing the firstrequest-based parsimony tree with one or more first network-basedparsimony trees might comprise comparing the first request-basedparsimony tree with one or more first network-based parsimony treesusing one or more GPUs, or the like.

Merely by way of example, in some cases, the first micro orchestratorand/or the computing system might identify a best-fit network-basedparsimony tree among the one or more first network-based parsimony treesbased on the fitness scores of the one or more first network-basedparsimony trees; might identify one or more first network resourcesamong a first plurality of network resources for providing the requestednetwork services, based at least in part on network resourcesrepresented within the identified best-fit network-based parsimony tree;and might allocate at least one first network resource among theidentified one or more first network resources for providing therequested network services. According to some embodiments, identifyingthe best-fit network-based parsimony tree might comprise identifying themost parsimonious first network-based parsimony tree for providing therequested network resources. That is, the first micro orchestratorand/or the computing system might identify the tree with the simplest(or least complicated) network characteristics or the tree with theshortest (or fewest) network routing requirements, or the like, thatenables allocation of the requested network services with the desiredcharacteristics and performance parameters. In some embodiments, atleast one of generating first network-based parsimony trees, comparingthe first request-based parsimony tree with the one or more firstnetwork-based parsimony tree, identifying the best-fit network-basedparsimony tree, or identifying the one or more first network resourcesmay be performed using one or more of at least one ML system, at leastone AI systems, or at least one NN system, and/or the like.

In some embodiments, the first micro orchestrator and/or the computingsystem might apply a first filter to at least one first network-basedparsimony tree among the one or more first network-based parsimony treesto filter out one or more characteristics or one or moresub-characteristics, prior to comparing the first request-basedparsimony tree with the one or more first network-based parsimony trees.According to some embodiments, the characteristics of the third andfourth portions might include color-codes embodied as a colortemperature or range of colors for each portion or for each parsimonytree that is indicative of characteristics or performance parametersincluding one or more of latency, jitter, packet loss, number of hops,bandwidth, utilization, capacity, or proximity, and/or the like. In suchcases, alternative or additional to applying the first filter, the firstmicro orchestrator and/or the computing system might apply a secondfilter to at least one first network-based parsimony tree among the oneor more first network-based parsimony trees to change the colortemperature based on changes in measured network metrics.

According to some embodiments, the first micro orchestrator and/or thecomputing system might receive updated measured network metrics; might,in response to receiving the updated measured network metrics, generatea plurality of updated first network-based parsimony trees; and mightreplace the plurality of first network-based parsimony trees in thedatastore with the plurality of updated first network-based parsimonytrees. In some embodiments, the updated measured network metrics mightbe received according to one of the following: on a periodic basis, on acontinual basis, on a random basis, or in response to a change innetwork characteristic or performance in at least one network resourcein a network, and/or the like. In some cases, each of the plurality ofupdated first network-based parsimony trees might be stored in thedatastore as an image file (e.g., .jpg file, .tiff file, .gif file, .bmpfile, .png file, .dwf file, .dwg file, .drw file, .stl file, .pdf file,.svg file, .cgm file, etc.).

In some embodiments, rather than a single request-based parsimony treebeing generated in response to receiving the request for networkservices, the first micro orchestrator and/or the computing system mightgenerate a plurality of first request-based parsimony trees, eachrepresenting a desired characteristic or performance parameter, and thesubsequent functions performed by the first orchestrator and/or thecomputing system might be performed on this plurality of firstrequest-based parsimony trees rather than the single request-basedparsimony tree.

Importantly, the use of parsimony trees for implementing intent-basedorchestration and automation of network functionalities, especiallycoupled with the use of GPUs and/or intelligent systems (e.g., machinelearning, AI, neural networks, etc.), results in less computationallyintense determination of intent compared with using CPUs or the like(with or without using intelligent systems), and thus enables moreefficient (or improved) intent-based orchestration and automation ofnetwork functionalities.

These and other functions of the system and method are described ingreater detail above with respect to the figures.

The following detailed description illustrates a few exemplaryembodiments in further detail to enable one of skill in the art topractice such embodiments. The described examples are provided forillustrative purposes and are not intended to limit the scope of theinvention.

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the described embodiments. It will be apparent to oneskilled in the art, however, that other embodiments of the presentinvention may be practiced without some of these specific details. Inother instances, certain structures and devices are shown in blockdiagram form. Several embodiments are described herein, and whilevarious features are ascribed to different embodiments, it should beappreciated that the features described with respect to one embodimentmay be incorporated with other embodiments as well. By the same token,however, no single feature or features of any described embodimentshould be considered essential to every embodiment of the invention, asother embodiments of the invention may omit such features.

Unless otherwise indicated, all numbers used herein to expressquantities, dimensions, and so forth used should be understood as beingmodified in all instances by the term “about.” In this application, theuse of the singular includes the plural unless specifically statedotherwise, and use of the terms “and” and “or” means “and/or” unlessotherwise indicated. Moreover, the use of the term “including,” as wellas other forms, such as “includes” and “included,” should be considerednon-exclusive. Also, terms such as “element” or “component” encompassboth elements and components comprising one unit and elements andcomponents that comprise more than one unit, unless specifically statedotherwise.

Various embodiments described herein, while embodying (in some cases)software products, computer-performed methods, and/or computer systems,represent tangible, concrete improvements to existing technologicalareas, including, without limitation, network configuration technology,network resource allocation technology, and/or the like. In otheraspects, certain embodiments, can improve the functioning of a computeror network system itself (e.g., computing devices or systems that formparts of the network, computing devices or systems, network elements orthe like for performing the functionalities described below, etc.), forexample, by receiving, with a computing system, a request for networkservices from a user device associated with a customer, the request fornetwork services comprising desired characteristics and performanceparameters for the requested network services, without informationregarding any of specific hardware, specific hardware type, specificlocation, or specific network for providing the requested networkservices; in response to receiving the request for network services,generating, with the computing system, a first request-based parsimonytree based at least in part on the desired characteristics andperformance parameters contained in the request for network services,the first request-based parsimony tree being a graphical representationcomprising an end-point of a first portion representing deliverylocation of the requested network services, an endpoint of each of oneor more second portions that connect with the first portion representinga service provider site, each intersection between two or more secondportions or between the first portion and one of the second portionsrepresenting a network resource node, and characteristics of the firstand second portions representing the desired characteristics andperformance parameters contained in the request for network services;accessing, with the computing system and from a datastore, a pluralityof first network-based parsimony trees, each of the plurality of firstnetwork-based parsimony trees being generated based on measured networkmetrics, each first network-based parsimony tree being a graphicalrepresentation comprising an end-point of a third portion representingthe delivery location of the requested network services, an endpoint ofeach of one or more fourth portions that connect with the third portionrepresenting a service provider site, each intersection between two ormore fourth portions or between the third portion and one of the fourthportions representing a network resource node, and characteristics ofthe third and fourth portions representing measured characteristics andperformance parameters based on the measured network metrics; comparing,with the computing system, the first request-based parsimony tree withone or more first network-based parsimony trees among the plurality offirst network-based parsimony trees to determine a fitness score foreach first network-based parsimony tree, each fitness score being avalue indicative of a level of heuristic matching between the firstrequest-based parsimony tree with one of the one or more firstnetwork-based parsimony trees; identifying, with the computing system, abest-fit network-based parsimony tree among the one or more firstnetwork-based parsimony trees based on the fitness scores of the one ormore first network-based parsimony trees; identifying, with thecomputing system, one or more first network resources among a firstplurality of network resources for providing the requested networkservices, based at least in part on network resources represented withinthe identified best-fit network-based parsimony tree; and allocating,with the computing system, at least one first network resource among theidentified one or more first network resources for providing therequested network services; and/or the like.

In particular, to the extent any abstract concepts are present in thevarious embodiments, those concepts can be implemented as describedherein by devices, software, systems, and methods that involve specificnovel functionality (e.g., steps or operations), such as, in response toreceiving a request for network services that comprises desiredcharacteristics and performance parameters for the requested networkservices without information regarding specific hardware, hardware type,location, or network, generating, with a computing system, arequest-based parsimony tree based on the desired characteristics andperformance parameters; accessing, with the computing system and from adatastore, a plurality of network-based parsimony trees that are eachgenerated based on measured network metrics; comparing, with thecomputing system, the request-based parsimony tree with each of one ormore network-based parsimony trees to determine a fitness score for eachnetwork-based parsimony tree; identifying, with the computing system, abest-fit network-based parsimony tree based on the fitness scores;identifying and allocating, with the computing system, network resourcesbased on the identified best-fit network-based parsimony tree, forproviding the requested network services; and/or the like, to name a fewexamples, that extend beyond mere conventional computer processingoperations. These functionalities can produce tangible results outsideof the implementing computer system, including, merely by way ofexample, ability to improve network functions, network resourceallocation and utilization, network orchestration and automation, and/orthe like, in various embodiments based on the intent-driven requeststhat are tagged as metadata or the like in resource databases fornetwork resources used to fulfill network service requests by customers,and based on the multi-tiered orchestration and automation of suchintent-driven requests, which may be observed or measured by customersand/or service providers. Further, the use of parsimony trees forimplementing intent-based orchestration and automation of networkfunctionalities, especially coupled with the use of GPUs and/orintelligent systems (e.g., machine learning, AI, neural networks, etc.),results in less computationally intense determination of intent comparedwith using CPUs or the like (with or without using intelligent systems),and thus enables more efficient (or improved) intent-based orchestrationand automation of network functionalities.

In an aspect, a method might comprise receiving, with a macroorchestrator over a network, a request for network services from a userdevice associated with a customer, the request for network servicescomprising desired characteristics and performance parameters for therequested network services, without information regarding any ofspecific hardware, specific hardware type, specific location, orspecific network for providing the requested network services; sending,with the macro orchestrator and to a first micro orchestrator among aplurality of micro orchestrators, the received request for networkservices, wherein the macro orchestrator automates, manages, or controlseach of the plurality of micro orchestrators, while each microorchestrator automates, manages, or controls at least one of a pluralityof domain managers or a plurality of network resources; in response toreceiving the request for network services, identifying, with the firstmicro orchestrator, one or more first network resources among a firstplurality of network resources for providing the requested networkservices, based at least in part on the desired characteristics andperformance parameters, and based at least in part on a determinationthat the one or more network resources are capable of providing networkservices having the desired characteristics and performance parameters;and allocating, with the first micro orchestrator, at least one firstnetwork resource among the identified one or more first networkresources for providing the requested network services.

In some embodiments, the macro orchestrator and the plurality of microorchestrators might each comprise one of a server computer over anetwork, a cloud-based computing system over a network, or a distributedcomputing system, and/or the like. In some cases, the desiredperformance parameters might comprise at least one of a maximum latency,a maximum jitter, a maximum packet loss, or a maximum number of hops,and/or the like. In some instances, the desired characteristics mightcomprise at least one of requirement for network equipment to begeophysically proximate to the user device associated with the customer,requirement for network equipment to be located within a firstgeophysical location, requirement to avoid routing network trafficthrough a second geophysical location, requirement to route networktraffic through a third geophysical location, requirement to exclude afirst type of network resources from fulfillment of the requestednetwork services, requirement to include a second type of networkresources for fulfillment of the requested network services, requirementto fulfill the requested network services based on a single goalindicated by the customer, or requirement to fulfill the requestednetwork services based on multiple goals indicated by the customer,and/or the like.

According to some embodiments, the method might further comprisereceiving, with the first micro orchestrator and from one or more firstdomain managers among a first plurality of domain managers incommunication with the first micro orchestrator, data regarding thefirst plurality of network resources that are automated, managed, orcontrolled by each of the one or more first domain managers. In suchcases, identifying, with the first micro orchestrator, one or more firstnetwork resources among a first plurality of network resources forproviding the requested network services might comprise identifying,with the first micro orchestrator, one or more first network resourcesamong a first plurality of network resources for providing the requestednetwork services, based at least in part on the data regarding the oneor more first network resources, based at least in part on the desiredcharacteristics and performance parameters, and based at least in parton a determination that the one or more network resources are capable ofproviding network services having the desired characteristics andperformance parameters.

In some embodiments, allocating, with the first micro orchestrator, atleast one first network resource among the identified one or more firstnetwork resources for providing the requested network services mightcomprise: sending, with the first micro orchestrator, commands to atleast one first domain manager among the one or more first domainmanagers that automate, manage, or control the at least one firstnetwork resource; and in response to receiving the commands from thefirst micro orchestrator: determining, with the at least one firstdomain manager, an intent based at least in part on the desiredcharacteristics and performance parameters as comprised in the requestfor network services; generating and sending, with the at least onefirst domain manager, device language instructions for allocating the atleast one first network resource; and implementing, with the at leastone first domain manager, the at least one first network resource on theuser device associated with the customer, to provide the requestednetwork services.

According to some embodiments, the macro orchestrator might comprise abusiness orchestrator, wherein the first micro orchestrator mightcomprise a network resource orchestrator, wherein the first plurality ofdomain managers each might comprise one of a physical network function(“PNF”) domain manager or a virtual network function (“VNF”) domainmanager, wherein the first plurality of domain managers each automates,manages, or controls each of a plurality of network resources located onone or more network devices in the network. Alternatively, oradditionally, the macro orchestrator might comprise a businessorchestrator, wherein the first micro orchestrator might comprise acompute resource orchestrator, wherein the identified one or more firstnetwork resources might comprise a plurality of compute resources,wherein the first plurality of domain managers each might comprise oneof a compute domain manager, a memory domain manager, or a storagedomain manager, and/or the like, wherein the first plurality of domainmanagers each automates, manages, or controls each of the plurality ofcompute resources located on at least one of one or more centralprocessing unit (“CPU”) pools, one or more graphics processing unit(“GPU”) pools, one or more random access memory (“RAM”) pools, or one ormore data storage pools, and/or the like. In some cases, the dataregarding the first plurality of network resources might be analyzedafter being received by the first micro orchestrator in response to oneof a pull data distribution instruction, a push data distributioninstruction, or a hybrid push-pull data distribution instruction, and/orthe like.

In some embodiments, the method might further comprise updating, withone of the macro orchestrator or the first micro orchestrator, aresource database with information indicating that the at least onefirst network resource has been allocated for providing the requestednetwork services and with information indicative of the desiredcharacteristics and performance parameters as comprised in the requestfor network services.

According to some embodiments, the method might further comprisedetermining, with an audit engine, whether each of the identified one ormore first network resources conforms with the desired characteristicsand performance parameters. In some instances, determining whether eachof the identified one or more first network resources conforms with thedesired characteristics and performance parameters might comprisedetermining, with the audit engine, whether each of the identified oneor more first network resources conforms with the desiredcharacteristics and performance parameters on a periodic basis or inresponse to a request to perform an audit. Alternatively, oradditionally, determining whether each of the identified one or morefirst network resources conforms with the desired characteristics andperformance parameters might comprise determining, with the auditengine, whether each of the identified one or more first networkresources conforms with the desired characteristics and performanceparameters, by: measuring one or more network performance metrics ofeach of the identified one or more first network resources; comparing,with the audit engine, the measured one or more network performancemetrics of each of the identified one or more first network resourceswith the desired performance parameters; determining characteristics ofeach of the identified one or more first network resources; andcomparing, with the audit engine, the determined characteristics of eachof the identified one or more first network resources with the desiredcharacteristics.

In such cases, each of the one or more network performance metrics mightcomprise at least one of quality of service (“QoS”) measurement data,platform resource data and metrics, service usage data, topology andreference data, historical network data, network usage trend data, orone or more of information regarding at least one of latency, jitter,bandwidth, packet loss, nodal connectivity, compute resources, storageresources, memory capacity, routing, operations support systems (“OSS”),or business support systems (“BSS”) or information regarding at leastone of fault, configuration, accounting, performance, or security(“FCAPS”), and/or the like.

In some embodiments, the method might further comprise, based on adetermination that at least one identified network resource among theidentified one or more first network resources fails to conform with thedesired performance parameters within first predetermined thresholds orbased on a determination that the determined characteristics of the atleast one identified network resource fails to conform with the desiredcharacteristics within second predetermined thresholds, performing oneof: reconfiguring, with the first micro orchestrator, the at least oneidentified network resource to provide the desired characteristics andperformance parameters; or reallocating, with the first microorchestrator, at least one other identified network resources among theidentified one or more first network resources for providing therequested network services.

In another aspect, a system might comprise a macro orchestrator and afirst micro orchestrator among a plurality of micro orchestrators. Themacro orchestrator might comprise at least one first processor and afirst non-transitory computer readable medium communicatively coupled tothe at least one first processor. The first non-transitory computerreadable medium might have stored thereon computer software comprising afirst set of instructions that, when executed by the at least one firstprocessor, causes the macro orchestrator to: receive, over a network, arequest for network services from a user device associated with acustomer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services; and send, to the first microorchestrator among the plurality of micro orchestrators, the receivedrequest for network services, wherein the macro orchestrator automates,manages, or controls each of the plurality of micro orchestrators, whileeach micro orchestrator automates, manages, or controls at least one ofa plurality of domain managers or a plurality of network resources.

The first micro orchestrator among the plurality of micro orchestratorsmight comprise at least one second processor and a second non-transitorycomputer readable medium communicatively coupled to the at least onesecond processor. The second non-transitory computer readable mediummight have stored thereon computer software comprising a second set ofinstructions that, when executed by the at least one second processor,causes the first micro orchestrator to: receive the request for networkservices from the macro orchestrator; in response to receiving therequest for network services, identify one or more first networkresources among a first plurality of network resources for providing therequested network services, based at least in part on the desiredcharacteristics and performance parameters, and based at least in parton a determination that the one or more network resources are capable ofproviding network services having the desired characteristics andperformance parameters; and allocate at least one first network resourceamong the identified one or more first network resources for providingthe requested network services.

In some embodiments, the macro orchestrator and the plurality of microorchestrators each might comprise one of a server computer over anetwork, a cloud-based computing system over a network, or a distributedcomputing system, and/or the like. In some cases, the desiredperformance parameters might comprise at least one of a maximum latency,a maximum jitter, a maximum packet loss, or a maximum number of hops,and/or the like. In some instances, the desired characteristics mightcomprise at least one of requirement for network equipment to begeophysically proximate to the user device associated with the customer,requirement for network equipment to be located within a firstgeophysical location, requirement to avoid routing network trafficthrough a second geophysical location, requirement to route networktraffic through a third geophysical location, requirement to exclude afirst type of network resources from fulfillment of the requestednetwork services, requirement to include a second type of networkresources for fulfillment of the requested network services, requirementto fulfill the requested network services based on a single goalindicated by the customer, or requirement to fulfill the requestednetwork services based on multiple goals indicated by the customer,and/or the like.

According to some embodiments, the system might further comprise one ormore first domain managers among a first plurality of domain managers incommunication with the first micro orchestrator. The second set ofinstructions, when executed by the at least one second processor, mightfurther cause the first micro orchestrator to: receive, from the one ormore first domain managers, data regarding the first plurality ofnetwork resources that are automated, managed, or controlled by each ofthe one or more first domain managers. In such cases, identifying one ormore first network resources among a first plurality of networkresources for providing the requested network services might compriseidentifying one or more first network resources among a first pluralityof network resources for providing the requested network services, basedat least in part on the data regarding the one or more first networkresources, based at least in part on the desired characteristics andperformance parameters, and based at least in part on a determinationthat the one or more network resources are capable of providing networkservices having the desired characteristics and performance parameters.

In some embodiments, the second set of instructions, when executed bythe at least one second processor, might further cause the first microorchestrator to: update a resource database with information indicatingthat the at least one first network resource has been allocated forproviding the requested network services and with information indicativeof the desired characteristics and performance parameters as comprisedin the request for network services.

According to some embodiments, the system might further comprise anaudit engine configured to determine whether each of the identified oneor more first network resources conforms with the desiredcharacteristics and performance parameters. In such cases, the secondset of instructions, when executed by the at least one second processor,might further cause the first micro orchestrator to: based on adetermination that at least one identified network resource among theidentified one or more first network resources fails to conform with thedesired performance parameters within first predetermined thresholds orbased on a determination that the determined characteristics of the atleast one identified network resource fails to conform with the desiredcharacteristics within second predetermined thresholds, perform one of:reconfiguring the at least one identified network resource to providethe desired characteristics and performance parameters; or reallocatingat least one other identified network resources among the identified oneor more first network resources for providing the requested networkservices.

In an aspect, a method might comprise receiving, with a computingsystem, a request for network services from a user device associatedwith a customer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services; and in response to receivingthe request for network services, generating, with the computing system,a first request-based parsimony tree based at least in part on thedesired characteristics and performance parameters contained in therequest for network services, the first request-based parsimony treebeing a graphical representation comprising an end-point of a firstportion representing delivery location of the requested networkservices, an endpoint of each of one or more second portions thatconnect with the first portion representing a service provider site,each intersection between two or more second portions or between thefirst portion and one of the second portions representing a networkresource node, and characteristics of the first and second portionsrepresenting the desired characteristics and performance parameterscontained in the request for network services. The method might alsocomprise accessing, with the computing system and from a datastore, aplurality of first network-based parsimony trees, each of the pluralityof first network-based parsimony trees being generated based on measurednetwork metrics, each first network-based parsimony tree being agraphical representation comprising an end-point of a third portionrepresenting the delivery location of the requested network services, anendpoint of each of one or more fourth portions that connect with thethird portion representing a service provider site, each intersectionbetween two or more fourth portions or between the third portion and oneof the fourth portions representing a network resource node, andcharacteristics of the third and fourth portions representing measuredcharacteristics and performance parameters based on the measured networkmetrics.

The method might further comprise comparing, with the computing system,the first request-based parsimony tree with one or more firstnetwork-based parsimony trees among the plurality of first network-basedparsimony trees to determine a fitness score for each firstnetwork-based parsimony tree, each fitness score being a valueindicative of a level of heuristic matching between the firstrequest-based parsimony tree with one of the one or more firstnetwork-based parsimony trees; identifying, with the computing system, abest-fit network-based parsimony tree among the one or more firstnetwork-based parsimony trees based on the fitness scores of the one ormore first network-based parsimony trees; identifying, with thecomputing system, one or more first network resources among a firstplurality of network resources for providing the requested networkservices, based at least in part on network resources represented withinthe identified best-fit network-based parsimony tree; and allocating,with the computing system, at least one first network resource among theidentified one or more first network resources for providing therequested network services.

In some embodiments, the computing system might comprise one of a servercomputer over a network, one or more graphics processing units (“GPUs”),a cloud-based computing system over a network, or a distributedcomputing system, and/or the like. In some cases, the desiredperformance parameters might comprise at least one of a maximum latency,a maximum jitter, a maximum packet loss, a maximum cost, or a maximumnumber of hops, and/or the like. In some instances, the desiredcharacteristics might comprise at least one of requirement for networkequipment to be geophysically proximate to the user device associatedwith the customer, requirement for network equipment to be locatedwithin a first geophysical location, requirement to avoid routingnetwork traffic through a second geophysical location, requirement toroute network traffic through a third geophysical location, requirementto exclude a first type of network resources from fulfillment of therequested network services, requirement to include a second type ofnetwork resources for fulfillment of the requested network services,requirement to fulfill the requested network services based on a singlegoal indicated by the customer, or requirement to fulfill the requestednetwork services based on multiple goals indicated by the customer,and/or the like.

According to some embodiments, the first portion of the firstrequest-based parsimony tree and the third portion of each firstnetwork-based parsimony tree might each be represented by a trunk, whilethe one or more second portions of the first request-based parsimonytree and the one or more fourth portions of each first network-basedparsimony tree might each be represented by a branch, and, in eachparsimony tree, one or more branches might connect with the trunk. Insome cases, in each of at least one parsimony tree, two or more branchesmight connect with each other via one or more connector branches and viathe trunk.

In some embodiments, the characteristics of the first and secondportions and the characteristics of the third and fourth portions mightcomprise at least one of thickness of each portion, length of eachportion from the delivery location of the requested network services,number of network resource nodes on each portion, color-code of eachportion, number of second or fourth portions, angle of each second orfourth portion relative to the first or third portion, number of anyconnector portions between two or more second or fourth portions,relative location of any connector portions between two or more secondor fourth portions, length of any connector portions between two or moresecond or fourth portions, or angle of any connector portions betweentwo or more second or fourth portions, and/or the like. In someinstances, the characteristics of the first and second portions and thecharacteristics of the third and fourth portions might represent one ormore of latency, jitter, packet loss, number of hops, bandwidth,utilization, capacity, or proximity, and/or the like.

According to some embodiments, the method might further compriseapplying, with the computing system, a first filter to at least onefirst network-based parsimony tree among the one or more firstnetwork-based parsimony trees to filter out one or more characteristicsor one or more sub-characteristics, prior to comparing the firstrequest-based parsimony tree with the one or more first network-basedparsimony trees. Alternatively, or additionally, the characteristics ofthe third and fourth portions might comprise color-codes embodied as acolor temperature or range of colors for each portion or for eachparsimony tree that is indicative of characteristics or performanceparameters including one or more of latency, jitter, packet loss, numberof hops, bandwidth, utilization, capacity, or proximity, and/or thelike. In such cases, the method might further comprise applying, withthe computing system, a second filter to at least one firstnetwork-based parsimony tree among the one or more first network-basedparsimony trees to change the color temperature based on changes inmeasured network metrics.

In some embodiments, generating the first request-based parsimony treemight comprise generating a plurality of first request-based parsimonytrees, each representing a desired characteristic or performanceparameter. In some cases, the plurality of first network-based parsimonytrees might comprise a plurality of first network-based parsimony treescorresponding to each of the desired characteristics and performanceparameters, each of the plurality of first network-based parsimony treesbeing generated based on measured network metrics. In some instances,comparing the first request-based parsimony tree with one or more firstnetwork-based parsimony trees among the plurality of first network-basedparsimony trees might comprise comparing, with the computing system,each first request-based parsimony tree representing one of the desiredcharacteristics and performance parameters with a correspondingplurality of first network-based parsimony trees. In some cases,identifying the best-fit network-based parsimony tree might compriseidentifying, with the computing system, the best-fit network-basedparsimony tree corresponding to each of the desired characteristics andperformance parameters. In some instances, identifying the one or morefirst network resources might be based at least in part on the networkresources represented within the identified best-fit network-basedparsimony trees.

According to some embodiments, comparing the first request-basedparsimony tree with one or more first network-based parsimony treesmight comprise comparing the first request-based parsimony tree with oneor more first network-based parsimony trees using one or more graphicsprocessing units (“GPUs”). Merely by way of example, in some cases, eachportion of each first network-based parsimony tree might be representedby a second network-based parsimony tree among a plurality of secondnetwork-based parsimony tree that is indicative of characteristics andperformance parameters of that portion, wherein each portion of eachsecond network-based parsimony tree might be represented by a thirdnetwork-based parsimony tree among a plurality of third network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion.

In some embodiments, the method might further comprise receiving, withthe computing system, updated measured network metrics; and in responseto receiving the updated measured network metrics, generating, with thecomputing system, a plurality of updated first network-based parsimonytrees, and replacing, with the computing system, the plurality of firstnetwork-based parsimony trees in the datastore with the plurality ofupdated first network-based parsimony trees. In some instances, theupdated measured network metrics might be received according to one ofthe following: on a periodic basis, on a continual basis, on a randombasis, or in response to a change in network characteristic orperformance in at least one network resource in a network, and/or thelike. In some cases, each of the plurality of updated firstnetwork-based parsimony trees might be stored in the datastore as animage file.

According to some embodiments, identifying the best-fit network-basedparsimony tree might comprise identifying the most parsimonious firstnetwork-based parsimony tree for providing the requested networkresources. Merely by way of example, in some cases, at least one ofgenerating first network-based parsimony trees, comparing the firstrequest-based parsimony tree with the one or more first network-basedparsimony tree, identifying the best-fit network-based parsimony tree,or identifying the one or more first network resources might beperformed using one or more of at least one machine learning (“ML”)system, at least one artificial intelligence (“AI”) systems, or at leastone neural network (“NN”) system, and/or the like.

In some embodiments, receiving the request for network services from theuser device associated with the customer might comprise receiving, witha macro orchestrator over a network, a request for network services froma user device associated with a customer. In some instances, generatingthe first request-based parsimony tree might comprise generating, with afirst micro orchestrator among a plurality of micro orchestrators, afirst request-based parsimony tree. In some cases, accessing theplurality of first network-based parsimony trees from the datastoremight comprise accessing, with the first micro orchestrator and from thedatastore, a plurality of first network-based parsimony trees. In someinstances, comparing the first request-based parsimony tree with the oneor more first network-based parsimony trees might comprise comparing,with the first micro orchestrator, the first request-based parsimonytree with one or more first network-based parsimony trees. In somecases, identifying the best-fit network-based parsimony tree among theone or more first network-based parsimony trees might compriseidentifying, with the first micro orchestrator, a best-fit network-basedparsimony tree among the one or more first network-based parsimonytrees. In some instances, identifying the one or more first networkresources for providing the requested network services might compriseidentifying, with the first micro orchestrator, one or more firstnetwork resources among a first plurality of network resources forproviding the requested network services. In some cases, allocating theat least one first network resource among the identified one or morefirst network resources for providing the requested network servicesmight comprise allocating, with the first micro orchestrator, at leastone first network resource among the identified one or more firstnetwork resources for providing the requested network services

In another aspect, a system might comprise a computing system, whichmight comprise at least one first processor and a first non-transitorycomputer readable medium communicatively coupled to the at least onefirst processor. The first non-transitory computer readable medium mighthave stored thereon computer software comprising a first set ofinstructions that, when executed by the at least one first processor,causes the computing system to: receive a request for network servicesfrom a user device associated with a customer, the request for networkservices comprising desired characteristics and performance parametersfor the requested network services, without information regarding any ofspecific hardware, specific hardware type, specific location, orspecific network for providing the requested network services; inresponse to receiving the request for network services, generate a firstrequest-based parsimony tree based at least in part on the desiredcharacteristics and performance parameters contained in the request fornetwork services, the first request-based parsimony tree being agraphical representation comprising an end-point of a first portionrepresenting delivery location of the requested network services, anendpoint of each of one or more second portions that connect with thefirst portion representing a service provider site, each intersectionbetween two or more second portions or between the first portion and oneof the second portions representing a network resource node, andcharacteristics of the first and second portions representing thedesired characteristics and performance parameters contained in therequest for network services; access, from a datastore, a plurality offirst network-based parsimony trees, each of the plurality of firstnetwork-based parsimony trees being generated based on measured networkmetrics, each first network-based parsimony tree being a graphicalrepresentation comprising an end-point of a third portion representingthe delivery location of the requested network services, an endpoint ofeach of one or more fourth portions that connect with the third portionrepresenting a service provider site, each intersection between two ormore fourth portions or between the third portion and one of the fourthportions representing a network resource node, and characteristics ofthe third and fourth portions representing measured characteristics andperformance parameters based on the measured network metrics; comparethe first request-based parsimony tree with one or more firstnetwork-based parsimony trees among the plurality of first network-basedparsimony trees to determine a fitness score for each firstnetwork-based parsimony tree, each fitness score being a valueindicative of a level of heuristic matching between the firstrequest-based parsimony tree with one of the one or more firstnetwork-based parsimony trees; identify a best-fit network-basedparsimony tree among the one or more first network-based parsimony treesbased on the fitness scores of the one or more first network-basedparsimony trees; identify one or more first network resources among afirst plurality of network resources for providing the requested networkservices, based at least in part on network resources represented withinthe identified best-fit network-based parsimony tree; and allocate atleast one first network resource among the identified one or more firstnetwork resources for providing the requested network services.

In some embodiments, the computing system comprises one of a servercomputer over a network, one or more graphics processing units (“GPUs”),a cloud-based computing system over a network, or a distributedcomputing system, and/or the like.

In yet another aspect, a method might comprise generating, with acomputing system, one or more parsimony trees, based on networktelemetry data of one or more networks, wherein each parsimony tree is agraphical representation of characteristics and performance parametersbased on the network telemetry data of the one or more networks; andperforming, with the computing system, network orchestration andautomation based on the generated one or more parsimony trees.

Various modifications and additions can be made to the embodimentsdiscussed without departing from the scope of the invention. Forexample, while the embodiments described above refer to particularfeatures, the scope of this invention also includes embodiments havingdifferent combination of features and embodiments that do not includeall of the above described features.

Specific Exemplary Embodiments

We now turn to the embodiments as illustrated by the drawings. FIGS. 1-8illustrate some of the features of the method, system, and apparatus forimplementing network services orchestration, and, more particularly, tomethods, systems, and apparatuses for implementing intent-basedmulti-tiered orchestration and automation and/or implementingintent-based orchestration using network parsimony trees, as referred toabove. The methods, systems, and apparatuses illustrated by FIGS. 1-8refer to examples of different embodiments that include variouscomponents and steps, which can be considered alternatives or which canbe used in conjunction with one another in the various embodiments. Thedescription of the illustrated methods, systems, and apparatuses shownin FIGS. 1-8 is provided for purposes of illustration and should not beconsidered to limit the scope of the different embodiments.

With reference to the figures, FIG. 1 is a schematic diagramillustrating a system 100 for implementing intent-based multi-tieredorchestration and automation and/or implementing intent-basedorchestration using network parsimony trees, in accordance with variousembodiments.

In the non-limiting embodiment of FIG. 1, system 100 might comprise amacro orchestrator 105 and one or more micro orchestrators in serviceprovider network(s) 115. In some embodiments, the macro orchestrator andthe one or more micro orchestrators might each include, but is notlimited to, one of a server computer over a network, a cloud-basedcomputing system over a network, or a distributed computing system,and/or the like. The macro orchestrator 105 might receive (either viawired or wireless connection) a request for network services from acustomer 120, via one or more user devices 125 a-125 n (collectively,“user devices 125” or the like), via access network 130. The one or moreuser devices 125 might include, without limitation, at least one of asmart phone, a mobile phone, a tablet computer, a laptop computer, adesktop computer, and/or the like. The request for network servicesmight include desired characteristics and performance parameters for therequested network services, without information regarding any ofspecific hardware, specific hardware type, specific location, orspecific network for providing the requested network services.

The desired performance parameters, in some embodiments, might include,but is not limited to, at least one of a maximum latency, a maximumjitter, a maximum packet loss, or a maximum number of hops, and/or thelike. The desired characteristics, according to some embodiments, mightinclude, without limitation, at least one of requirement for networkequipment to be geophysically proximate to the user device associatedwith the customer, requirement for network equipment to be locatedwithin a first geophysical location, requirement to avoid routingnetwork traffic through a second geophysical location, requirement toroute network traffic through a third geophysical location, requirementto exclude a first type of network resources from fulfillment of therequested network services, requirement to include a second type ofnetwork resources for fulfillment of the requested network services,requirement to fulfill the requested network services based on a singlegoal indicated by the customer, or requirement to fulfill the requestednetwork services based on multiple goals indicated by the customer,and/or the like.

System 100 might further comprise one or more domain managers 135 andnetwork resources 140 that may be disposed, and/or communicativelycoupled to, networks 145 a-145 n (collectively, “networks 145” or thelike) and/or networks 150 a-150 n (collectively, “networks 150” or thelike). The one or more domain managers 135 might, in some cases, includedomain managers 135 a in network(s) 145 or domain managers 135 b innetwork(s) 150, or the like. In some embodiments, the macro orchestrator105 might include, without limitation, a business orchestrator, or thelike. In some instances, the one or more micro orchestrators 110 mighteach include, but is not limited to, one of a network resourceorchestrator(s), a compute resource orchestrator(s), a billing resourceorchestrator(s), or an order orchestrator(s), or the like. In somecases, the one or more domain managers might each include, withoutlimitation, one of a physical network function (“PNF”) domainmanager(s), a virtual network function (“VNF”) domain manager(s), acompute domain manager(s), a memory domain manager(s), or a storagedomain manager(s), and/or the like.

The macro orchestrator 105 might automate, manage, and/or control eachof the one or more micro orchestrators 110, while each microorchestrator 110 might automate, manage, and/or control at least one ofa plurality of domain managers or a plurality of network resources. Forinstance, a network resource orchestrator might automate, manage, and/orcontrol one or more of at least one PNF domain manager or at least oneVNF domain manager, while the at least one PNF domain manager mightautomate, manage, and/or control each of a plurality of physical networkresources located in devices in networks under its control, and the atleast one VNF domain manager might automate, manage, and/or control eachof a plurality of virtual network resources located in devices innetworks under its control. Similarly, a compute resource orchestratormight automate, manage, and/or control one or more of at least onecompute domain manager, at least one memory domain manager, or at leastone storage domain manager, while the at least one compute domainmanager might automate, manage, and/or control each of a plurality ofcompute resources in one or more compute pools (e.g., central processingunit (“CPU”) pools, graphics processing unit (“GPU”) pools, or thelike), and the at least one memory domain manager might automate,manage, and/or control each of a plurality of memory resources in one ormore memory pools (e.g., random access memory (“RAM”) pools, or thelike), while the at least one storage domain manager might automate,manage, and/or control each of a plurality of storage resources in oneor more storage pools, or the like.

The micro orchestrator(s) 110 might analyze first metadata regardingresource attributes and characteristics of a plurality of unassignednetwork resources to identify one or more network resources 140 amongthe plurality of unassigned network resources for providing therequested network services, the first metadata having been striped toentries of the plurality of unassigned network resources in a resourcedatabase, which might include, without limitation, resource inventorydatabase 155, intent metadata database 160, data lake 180, and/or thelike. Based on the analysis, the micro orchestrator(s) 110 mightallocate at least one identified network resource 140 among theidentified one or more network resources 140 for providing the requestednetwork services. The micro orchestrator(s) 110 might stripe the entrywith second metadata indicative of the desired characteristics andperformance parameters as comprised in the request for network services.In some cases, striping the entry with the second metadata mightcomprise striping the entry in the resource inventory database 155.Alternatively, striping the entry with the second metadata mightcomprise striping or adding an entry in the intent metadata inventory160, which might be part of resource inventory database 155 or might bephysically separate (or logically partitioned) from the resourceinventory database 155, or the like. In some cases, the first metadatamight be analyzed after being received by the computing system inresponse to one of a pull data distribution instruction, a push datadistribution instruction, or a hybrid push-pull data distributioninstruction, and/or the like.

Once the at least one identified network resource 140 has been allocatedor assigned, the micro orchestrator(s) 110 might update an activeinventory database 165 with such information—in some cases, by adding anentry in the active inventory database 165 with information indicatingthat the at least one identified network resource 140 has been allocatedto provide particular requested network service(s) to customer 120. Insome embodiments, the micro orchestrator(s) 110 might stripe the addedentry in the active inventory database 165 with a copy of the secondmetadata indicative of the desired characteristics and performanceparameters as comprised in the request for network services. In someinstances, the resource inventory database 155 might store an equipmentrecord that lists every piece of inventory that is accessible by themicro orchestrator(s) 110 (either already allocated for fulfillment ofnetwork services to existing customers or available for allocation forfulfillment of new network services to existing or new customers). Theactive inventory database 165 might store a circuit record listing theactive inventory that are being used for fulfilling network services.The data lake 180 might store a customer record that lists the servicerecord of customer, and/or the like.

According to some embodiments, system 100 might further comprise qualityof service test and validate server or audit engine 170, which performsmeasurement and/or collection of network performance metrics for atleast one of the one or more network resources 140 and/or the one ormore networks 145 and/or 150, and/or which performs auditing todetermine whether each of the identified one or more network resources140 conforms with the desired characteristics and performanceparameters. In some cases, network performance metrics might include,without limitation, at least one of quality of service (“QoS”)measurement data, platform resource data and metrics, service usagedata, topology and reference data, historical network data, or networkusage trend data, and/or the like. Alternatively, or additionally,network performance metrics might include, but are not limited to, oneor more of information regarding at least one of latency, jitter,bandwidth, packet loss, nodal connectivity, compute resources, storageresources, memory capacity, routing, operations support systems (“OSS”),or business support systems (“BSS”) or information regarding at leastone of fault, configuration, accounting, performance, or security(“FCAPS”), and/or the like, which are described in greater detail in the'095, '244, and '884 applications, which have already been incorporatedherein by reference in their entirety. Also described in greater detailin the '095, '244, and '884 applications is how the intent-based systemallocates or reallocates resources based on a determination thatexisting resources are no longer able to provide the desiredcharacteristics and performance parameters.

In some embodiments, micro orchestrator(s) 110 might allocate one ormore network resources 140 from one or more first networks 145 a-145 nof a first set of networks 145 and/or from one or more second networks150 a-150 n of a second set of networks 150 for providing the requestednetwork services, based at least in part on the desired performanceparameters and/or based at least in part on a determination that the oneor more first networks is capable of providing network resources eachhaving the desired performance parameters. According to someembodiments, determination that the one or more first networks iscapable of providing network resources each having the desiredperformance parameters is based on one or more network performancemetrics of the one or more first networks at the time that the requestfor network services from a customer is received.

System 100 might further comprise one or more databases, including, butnot limited to, a platform resource database 175 a, a service usagedatabase 175 b, a topology and reference database 175 c, a QoSmeasurement database 175 d, and/or the like. The platform resourcedatabase 175 a might collect and store data related or pertaining toplatform resource data and metrics, or the like, while the service usagedatabase 175 b might collect and store data related or pertaining toservice usage data or service profile data, and the topology andreference database 175 c might collect and store data related orpertaining to topology and reference data. The QoS measurement database175 d might collect and store QoS data, network performance metrics,and/or results of the QoS test and validate process. Data stored in eachof at least one of the platform resource database 175 a, the serviceusage database 175 b, the topology and reference database 175 c, the QoSmeasurement database 175 d, and/or the like, collected in data lake 180,and the collective data or selected data from the data lake 180 are usedto perform optimization of network resource allocation (both physicaland/or virtual) using the micro orchestrator(s) 110 (and, in some cases,using an orchestration optimization engine (e.g., orchestrationoptimization engine 275 of FIG. 2 of the '244 and '884 applications), orthe like).

In some embodiments, determining whether each of the identified one ormore network resources conforms with the desired characteristics andperformance parameters might comprise determining, with the audit engine170, whether each of the identified one or more network resourcesconforms with the desired characteristics and performance parameters ona periodic basis or in response to a request to perform an audit.Alternatively, or additionally, determining whether each of theidentified one or more network resources conforms with the desiredcharacteristics and performance parameters might comprise determining,with the audit engine, whether each of the identified one or morenetwork resources conforms with the desired characteristics andperformance parameters, by: measuring one or more network performancemetrics of each of the identified one or more network resources;comparing, with the audit engine, the measured one or more networkperformance metrics of each of the identified one or more networkresources with the desired performance parameters; determiningcharacteristics of each of the identified one or more network resources;and comparing, with the audit engine, the determined characteristics ofeach of the identified one or more network resources with the desiredcharacteristics.

Based on a determination that at least one identified network resourceamong the identified one or more network resources fails to conform withthe desired performance parameters within first predetermined thresholdsor based on a determination that the determined characteristics of theat least one identified network resource fails to conform with thedesired characteristics within second predetermined thresholds, themicro orchestrator(s) 110 might perform one of: reconfiguring the atleast one identified network resource to provide the desiredcharacteristics and performance parameters; or reallocating at least oneother identified network resources among the identified one or morenetwork resources for providing the requested network services. In somecases, the micro orchestrator(s) 110 might perform one of reconfiguringthe at least one identified network resource or reallocating at leastone other identified network resources, based on a determination thatthe measured one or more network performance metrics of each of theidentified one or more network resources fails to match the desiredperformance parameters within third predetermined thresholds or based ona determination that the measured one or more network performancemetrics of each of the identified one or more network resources fails tomatch the desired performance parameters within fourth predeterminedthresholds.

According to some aspects, the macro orchestrator 105 might receive,over a network, a request for network services from a user deviceassociated with a customer, the request for network services comprisingdesired characteristics and performance parameters for the requestednetwork services, without information regarding any of specifichardware, specific hardware type, specific location, or specific networkfor providing the requested network services. The macro orchestrator 105might send, to a first micro orchestrator among a plurality of microorchestrators (e.g., the one or more micro orchestrators 110, or thelike), the received request for network services, where the macroorchestrator automates, manages, or controls each of the plurality ofmicro orchestrators, while each micro orchestrator automates, manages,or controls at least one of a plurality of domain managers (e.g., theone or more domain managers 135, or the like) or a plurality of networkresources (e.g., network resources 140, or the like). In response toreceiving the request for network services, the first micro orchestratormight identify one or more first network resources among a firstplurality of network resources for providing the requested networkservices, based at least in part on the desired characteristics andperformance parameters, and based at least in part on a determinationthat the one or more network resources are capable of providing networkservices having the desired characteristics and performance parameters.The first micro orchestrator might allocate at least one first networkresource among the identified one or more first network resources forproviding the requested network services.

In some embodiments, the first micro orchestrator might (continually,occasionally, randomly, or in response to a request for data, or thelike) receive, from one or more first domain managers among a firstplurality of domain managers in communication with the first microorchestrator, data regarding the first plurality of network resourcesthat are automated, managed, or controlled by each of the one or morefirst domain managers. In such cases, identifying, with the first microorchestrator, one or more first network resources among a firstplurality of network resources for providing the requested networkservices might comprise identifying, with the first micro orchestrator,one or more first network resources among a first plurality of networkresources for providing the requested network services, based at leastin part on the data regarding the one or more first network resources,based at least in part on the desired characteristics and performanceparameters, and based at least in part on a determination that the oneor more network resources are capable of providing network serviceshaving the desired characteristics and performance parameters.

According to some embodiments, allocating, with the first microorchestrator, at least one first network resource among the identifiedone or more first network resources for providing the requested networkservices might comprise: sending, with the first micro orchestrator,commands to at least one first domain manager among the one or morefirst domain managers that automate, manage, or control the at least onefirst network resource; and in response to receiving the commands fromthe first micro orchestrator: determining, with the at least one firstdomain manager, an intent based at least in part on the desiredcharacteristics and performance parameters as comprised in the requestfor network services; generating and sending, with the at least onefirst domain manager, device language instructions for allocating the atleast one first network resource; and implementing, with the at leastone first domain manager, the at least one first network resource on theuser device associated with the customer, to provide the requestednetwork services.

In some embodiments, one of the macro orchestrator or the first microorchestrator might update a resource database (e.g., resource inventorydatabase 155, intent metadata database 160, active inventory database165, and/or data lake 180, or the like) with information indicating thatthe at least one first network resource has been allocated for providingthe requested network services and with information indicative of thedesired characteristics and performance parameters as comprised in therequest for network services. In some cases, an audit engine (e.g.,audit engine 170, or the like) might determine whether each of theidentified one or more first network resources conforms with the desiredcharacteristics and performance parameters. In some instances,determining whether each of the identified one or more first networkresources conforms with the desired characteristics and performanceparameters might comprise determining, with the audit engine, whethereach of the identified one or more first network resources conforms withthe desired characteristics and performance parameters on a periodicbasis or in response to a request to perform an audit. Alternatively, oradditionally, determining whether each of the identified one or morefirst network resources conforms with the desired characteristics andperformance parameters might comprise determining, with the auditengine, whether each of the identified one or more first networkresources conforms with the desired characteristics and performanceparameters, by: measuring one or more network performance metrics ofeach of the identified one or more first network resources; comparing,with the audit engine, the measured one or more network performancemetrics of each of the identified one or more first network resourceswith the desired performance parameters; determining characteristics ofeach of the identified one or more first network resources; andcomparing, with the audit engine, the determined characteristics of eachof the identified one or more first network resources with the desiredcharacteristics.

In such cases, each of the one or more network performance metrics mightinclude, without limitation, at least one of quality of service (“QoS”)measurement data, platform resource data and metrics, service usagedata, topology and reference data, historical network data, networkusage trend data, or one or more of information regarding at least oneof latency, jitter, bandwidth, packet loss, nodal connectivity, computeresources, storage resources, memory capacity, routing, operationssupport systems (“OSS”), or business support systems (“BSS”) orinformation regarding at least one of fault, configuration, accounting,performance, or security (“FCAPS”), and/or the like.

According to some embodiments, based on a determination that at leastone identified network resource among the identified one or more firstnetwork resources fails to conform with the desired performanceparameters within first predetermined thresholds or based on adetermination that the determined characteristics of the at least oneidentified network resource fails to conform with the desiredcharacteristics within second predetermined thresholds, the first microorchestrator either might reconfigure the at least one identifiednetwork resource to provide the desired characteristics and performanceparameters; or might reallocate at least one other identified networkresources among the identified one or more first network resources forproviding the requested network services.

In some aspects, intent might further include, without limitation, pathintent, location intent, performance intent, and/or the like. Pathintent, for example, might include a requirement that network trafficmust be routed through a first particular geophysical location (e.g., acontinent, a country, a region, a state, a province, a city, a town, amountain range, etc.) and/or a requirement that network traffic must notbe routed through a second particular geophysical location, or the like.In such cases, a service commission engine might either add (and/or markas required) all paths through the first particular geophysical locationand all network resources that indicate that they are located in thefirst particular geophysical location, or remove (and/or mark asexcluded) all paths through the second particular geophysical locationand all network resources that indicate that they are located in thesecond particular geophysical location. The service commission enginemight use the required or non-excluded paths and network resources toidentify which paths and network resources to allocate to fulfillrequested network services. In some embodiments, the active inventorymight be marked so that any fix or repair action is also restricted andthat policy audits might be implemented to ensure no violations of pathintent actually occur.

Location intent, for instance, might include a requirement that networkresources that are used for fulfilling the requested network servicesare located in specific geographical locations (which are more specificcompared to the general geophysical locations described above). In suchcases, the inventory is required to include the metadata for the intent,then the service engine can perform the filtering and selection.Monitoring and/or restricting assets being reassigned may be performedusing location intent policy markings (or metadata) on the service.

Performance intent, for example, might include a requirement that therequested services satisfy particular performance parameters ormetrics—which might include, without limitation, maximum latency ordelay, maximum jitter, maximum packet loss, maximum number of hops,minimum bandwidth, nodal connectivity, minimum amount of computeresources for each allocated network resource, minimum amount of storageresources for each allocated network resource, minimum memory capacityfor each allocated network resource, fastest possible path, and/or thelike. In such cases, service conformance engine might use theperformance metrics (as measured by one or more nodes in the network,which in some cases might include the allocated network resource itself,or the like) between points (or network nodes) for filtering thecompliant inventory options, and/or might propose higher levels ofservice to satisfy the customer and/or cost level alignment, or thelike. Once the assignment portion of the engine has been performed, theactive inventory might be marked with the appropriate performance intentpolicy.

In some embodiments, a SS7 advanced intelligence framework (which mighthave a local number portability dip to get instructions from an externaladvanced intelligence function) can be adapted with intent-basedorchestration (as described herein) by putting a trigger (e.g., anexternal data dip, or the like) on the orchestrator between therequesting device or node (where the intent and intent criteria might besent) and the source of the external function, which might scrape theinventory database to make its instructions and/or solution sets for thefulfillment engine and then stripe metadata, and/or returns that to thenormal fulfillment engine.

In some aspects, one or more parsimony trees might be generated, basedon network telemetry data of one or more networks, where each parsimonytree might be a graphical representation of characteristics andperformance parameters based on the network telemetry data of the one ormore networks, and the system might perform network orchestration andautomation based on the generated one or more parsimony trees. Inparticular, the macro orchestrator 105 might receive, over a network, arequest for network services from a user device associated with acustomer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services. The macro orchestrator 105might send, to a first micro orchestrator among a plurality of microorchestrators (e.g., the one or more micro orchestrators 110, or thelike), the received request for network services, where the macroorchestrator automates, manages, or controls each of the plurality ofmicro orchestrators, while each micro orchestrator automates, manages,or controls at least one of a plurality of domain managers (e.g., theone or more domain managers 135, or the like) or a plurality of networkresources (e.g., network resources 140, or the like). In response toreceiving the request for network services, the first micro orchestratormight generate a first request-based parsimony tree based at least inpart on the desired characteristics and performance parameters containedin the request for network services.

According to some embodiments, the first request-based parsimony treemight be a graphical representation including, without limitation, anend-point of a first portion representing delivery location of therequested network services, an endpoint of each of one or more secondportions that connect with the first portion representing a serviceprovider site, each intersection between two or more second portions orbetween the first portion and one of the second portions representing anetwork resource node, and characteristics of the first and secondportions representing the desired characteristics and performanceparameters contained in the request for network services, and/or thelike. In some cases, the plurality of micro orchestrators might eachinclude, but is not limited to, one of a server computer over a network,a cloud-based computing system over a network, or a distributedcomputing system, and/or the like.

The first micro orchestrator might access, from a datastore, a pluralityof first network-based parsimony trees, each of the plurality of firstnetwork-based parsimony trees being generated based on measured networkmetrics. In some embodiments, each first network-based parsimony treemight be a graphical representation including, but not limited to, anend-point of a third portion representing the delivery location of therequested network services, an endpoint of each of one or more fourthportions that connect with the third portion representing a serviceprovider site, each intersection between two or more fourth portions orbetween the third portion and one of the fourth portions representing anetwork resource node, and characteristics of the third and fourthportions representing measured characteristics and performanceparameters based on the measured network metrics.

According to some embodiments, the first portion of the firstrequest-based parsimony tree and the third portion of each firstnetwork-based parsimony tree might each be represented by a trunk, whilethe one or more second portions of the first request-based parsimonytree and the one or more fourth portions of each first network-basedparsimony tree might each be represented by a branch, and, in eachparsimony tree, one or more branches might connect with the trunk. Insome cases, in each of at least one parsimony tree, two or more branchesmight connect with each other via one or more connector branches and viathe trunk, or the like. In some instances, each portion of each firstnetwork-based parsimony tree might be represented by a secondnetwork-based parsimony tree among a plurality of second network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion, where each portion of each secondnetwork-based parsimony tree might be represented by a thirdnetwork-based parsimony tree among a plurality of third network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion, and so on in a fractal-like manner.

In some embodiments, the characteristics of the first and secondportions and the characteristics of the third and fourth portions mightinclude, without limitation, at least one of thickness of each portion,length of each portion from the delivery location of the requestednetwork services, number of network resource nodes on each portion,color-code of each portion, number of second or fourth portions, angleof each second or fourth portion relative to the first or third portion,number of any connector portions between two or more second or fourthportions, relative location of any connector portions between two ormore second or fourth portions, length of any connector portions betweentwo or more second or fourth portions, or angle of any connectorportions between two or more second or fourth portions, and/or the like.In some instances, the characteristics of the first and second portionsand the characteristics of the third and fourth portions might representone or more of latency, jitter, packet loss, number of hops, bandwidth,utilization, capacity, or proximity, and/or the like.

According to some embodiments, the first micro orchestrator mightcompare the first request-based parsimony tree with one or more firstnetwork-based parsimony trees among the plurality of first network-basedparsimony trees to determine a fitness score for each firstnetwork-based parsimony tree. In some instances, each fitness scoremight be a value indicative of a level of heuristic matching (in somecases, embodied as a percentage match) between the first request-basedparsimony tree with one of the one or more first network-based parsimonytrees. In some embodiments, comparing the first request-based parsimonytree with one or more first network-based parsimony trees might comprisecomparing the first request-based parsimony tree with one or more firstnetwork-based parsimony trees using one or more GPUs, or the like.

Merely by way of example, in some cases, the first micro orchestratormight identify a best-fit network-based parsimony tree among the one ormore first network-based parsimony trees based on the fitness scores ofthe one or more first network-based parsimony trees; might identify oneor more first network resources among a first plurality of networkresources for providing the requested network services, based at leastin part on network resources represented within the identified best-fitnetwork-based parsimony tree; and might allocate at least one firstnetwork resource among the identified one or more first networkresources for providing the requested network services. According tosome embodiments, identifying the best-fit network-based parsimony treemight comprise identifying the most parsimonious first network-basedparsimony tree for providing the requested network resources. That is,the first micro orchestrator might identify the tree with the simplest(or least complicated) network characteristics or the tree with theshortest (or fewest) network routing requirements, or the like, thatenables allocation of the requested network services with the desiredcharacteristics and performance parameters. In some embodiments, atleast one of generating first network-based parsimony trees, comparingthe first request-based parsimony tree with the one or more firstnetwork-based parsimony tree, identifying the best-fit network-basedparsimony tree, or identifying the one or more first network resourcesmay be performed using one or more of at least one ML system, at leastone AI systems, or at least one NN system, and/or the like.

In some embodiments, the first micro orchestrator might apply a firstfilter to at least one first network-based parsimony tree among the oneor more first network-based parsimony trees to filter out one or morecharacteristics or one or more sub-characteristics, prior to comparingthe first request-based parsimony tree with the one or more firstnetwork-based parsimony trees. According to some embodiments, thecharacteristics of the third and fourth portions might includecolor-codes embodied as a color temperature or range of colors for eachportion or for each parsimony tree that is indicative of characteristicsor performance parameters including one or more of latency, jitter,packet loss, number of hops, bandwidth, utilization, capacity, orproximity, and/or the like. In such cases, alternative or additional toapplying the first filter, the first micro orchestrator might apply asecond filter to at least one first network-based parsimony tree amongthe one or more first network-based parsimony trees to change the colortemperature based on changes in measured network metrics.

According to some embodiments, the first micro orchestrator mightreceive updated measured network metrics; might, in response toreceiving the updated measured network metrics, generate a plurality ofupdated first network-based parsimony trees; and might replace theplurality of first network-based parsimony trees in the datastore withthe plurality of updated first network-based parsimony trees. In someembodiments, the updated measured network metrics might be receivedaccording to one of the following: on a periodic basis, on a continualbasis, on a random basis, or in response to a change in networkcharacteristic or performance in at least one network resource in anetwork, and/or the like. In some cases, each of the plurality ofupdated first network-based parsimony trees might be stored in thedatastore as an image file (e.g., .jpg file, .tiff file, .gif file, .bmpfile, .png file, .dwf file, .dwg file, .drw file, .stl file, .pdf file,.svg file, .cgm file, etc.).

In some embodiments, rather than a single request-based parsimony treebeing generated in response to receiving the request for networkservices, the first micro orchestrator might generate a plurality offirst request-based parsimony trees, each representing a desiredcharacteristic or performance parameter, and the subsequent functionsperformed by the first orchestrator might be performed on this pluralityof first request-based parsimony trees rather than the singlerequest-based parsimony tree.

These and other functions of the system 100 (and its components) aredescribed in greater detail below with respect to FIGS. 2-4.

FIGS. 2A and 2B (collectively, “FIG. 2”) are block diagrams illustratingvarious methods 200 and 200′ for implementing intent-based multi-tieredorchestration and automation, in accordance with various embodiments.While the techniques and procedures are depicted and/or described in acertain order for purposes of illustration, it should be appreciatedthat certain procedures may be reordered and/or omitted within the scopeof various embodiments. Moreover, while the method illustrated by FIG. 2can be implemented by or with (and, in some cases, are described belowwith respect to) the system 100 of FIG. 1, respectively (or componentsthereof), such methods may also be implemented using any suitablehardware (or software) implementation. Similarly, while system 100 ofFIG. 1 (or components thereof) can operate according to the methodillustrated by FIG. 2 (e.g., by executing instructions embodied on acomputer readable medium), the system 100 of FIG. 1 can each alsooperate according to other modes of operation and/or perform othersuitable procedures.

With reference to FIG. 2A, after receiving a request for networkservices from a customer (not shown)—the request for network servicescomprising desired performance parameters for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services [i.e., an “intent-based”request]—or to generally manage various networks (and optimization ofsuch various networks), method 200 might comprise macro orchestration(at block 205) that manages micro orchestration (at block 210) thatutilizes Network Functions Virtualization (“NFV”), software definednetworks (“SDNs”), and/or the like to determine what physical and/orvirtual network resources to allocate that meet the “intent” for networkresources having the desired performance parameters, for use by thecustomer, and/or to generally manage and/or optimize the variousnetworks (that are under the control of the macro orchestrator or microorchestrator(s)).

Method 200 might further comprise performing quality of service (“QoS”)testing and validation (at block 215) to commit to, or rollback from,the allocated network resources. The results of the QoS testing andvalidation (from block 215) are subsequently stored in data lake 220, aswell as in QoS measurement mesh data database 225 a. Data stored in eachof at least one of the QoS measurement mesh data database 225 a,topology and reference data database 225 b, service usage data database225 c, and platform resource data and metrics database 225 d arecollected in data lake 220, and the collective data or selected datafrom the data lake 220 may be used to perform fault detection andremediation assessment (at block 230). In some cases, the collectivedata or selected data from the data lake 220 are used by an artificialintelligence (“AI”) model training and rule development process (atblock 235) as a way to detect fault and to assess remediation. Method200 might further comprise calculating optimal re-routing taking intoaccount one or more of the collected data, the AI model training andrule development, the fault detection and remediation assessment, and/orthe QoS testing and validation results. Method 200 subsequently loopsback to macro orchestration (at block 205), and the processes at blocks205-240 repeat continually in a feedback loop-driven process to optimizeallocation of network resources for meeting the desired performanceparameters, as set out by the customer's “intent-based” request fornetwork services, and/or for generally managing and/or optimizing thevarious networks.

In some embodiments, the service aware optimized orchestration asdepicted in FIG. 2A may be implemented using collected feedback datathat are processed in batches. Alternatively, the service awareoptimized orchestration as depicted in FIG. 2A may be implemented usingreal-time streams of collected feedback data that are processed inreal-time.

FIG. 2B depicts an alternative method 200′ for implementing intent-basedmulti-tiered orchestration and automation. In FIG. 2B, method 200′comprises providing a customer with access to a service provisioningportal (e.g., via software application (“app”) that can be installed andrun on a user device (including, but not limited to, a smart phone, amobile phone, a laptop computer, a tablet computer, a desktop computer,and/or the like) that is associated with the user, via a web portal,and/or the like), and receiving, via the service provisioning portal, arequest for network services from a customer (at block 245) [i.e.,“activation” process]. The request for network services, as in theembodiment of FIG. 2A, might comprise desired performance parameters forthe requested network services, without information regarding any ofspecific hardware, specific hardware type, specific location, orspecific network for providing the requested network services [i.e., an“intent-based” request].

Method 200′ might comprise macro orchestration (at block 250) thatmanages micro orchestration (at block 255) that utilizes NetworkFunctions Virtualization (“NFV”), software defined networks (“SDNs”),and/or the like to determine what physical and/or virtual networkresources to allocate that meet the “intent” for network resourceshaving the desired performance parameters, for use by the customer,and/or to generally manage and/or optimize the various networks (thatare under the control of the macro orchestrator or microorchestrator(s)). In some embodiments, macro orchestration (at block250) might utilize orchestration optimization engine 275 to optimizemanagement of micro orchestration.

Method 200′ might further comprise performing quality of service (“QoS”)testing and validation (at block 260) to commit to or rollback theallocated network resources. According to some embodiments, microorchestration (at block 255) might utilize the results of the QoStesting and validation (from block 260) to immediately determine whatphysical and/or virtual network resources to allocate (or re-allocate)that meet the “intent” for network resources having the desiredperformance parameters, and/or to generally manage and/or optimize thevarious networks (that are under the control of the macro orchestratoror micro orchestrator(s)).

In general, the results of the QoS testing and validation (from block260) are subsequently stored in QoS measurement mesh data database 265a. Data stored in each of at least one of the QoS measurement mesh datadatabase 265 a, topology and reference data database 265 b, serviceusage data database 265 c, and platform resource data and metricsdatabase 265 d are collected in data lake 270, and the collective dataor selected data from the data lake 270 are used to perform optimizationof network resource allocation (both physical and/or virtual) usingorchestration optimization engine 275. In some cases, the collectivedata or selected data from the data lake 270 are used by an AI modeltraining and rule development process (at block 280) as a way to performoptimization of network resource allocation (both physical and/orvirtual) using orchestration optimization engine 275. The AI modeltraining and rule development process (at block 280) uses data from thedata lake 270 to improve the AI model training and rule development, ina continuous feedback loop. Method 200′ subsequently loops back to macroorchestration (at block 250), and the processes at blocks 250-280 repeatcontinually in a feedback loop-driven process to optimize allocation ofnetwork resources (both physical and/or virtual) for meeting the desiredperformance parameters, as set out by the customer's “intent-based”request for network services.

In some embodiments, the service aware optimized orchestration asdepicted in FIG. 2B may be implemented using collected feedback datathat are processed in batches. Alternatively, the service awareoptimized orchestration as depicted in FIG. 2B may be implemented usingreal-time streams of collected feedback data that are processed inreal-time.

FIG. 3 is a schematic diagram illustrating another system 300 forimplementing intent-based multi-tiered orchestration and automation, inaccordance with various embodiments. For simplifying illustration, FIG.3 shows a non-limiting example with one user device associated with asingle customer sending a request for network services. In practice,multiple devices associated with multiple customers would send requestsfor network services, and the system 300 would handle intent-basedmulti-tiered orchestration and automation for all such requestssimultaneously or concurrently, taking into account all aspects of therequests to ensure optimal operation of the network and optimalallocation of resources to provide the requested services to each of therequesting user devices.

With reference to the non-limiting embodiment of FIG. 3, system 300might comprise a business orchestrator 305 (i.e., a macro orchestrator),a user device 310 associated with a customer 315, a business informationdatabase 320, a network resource orchestrator(s) 325 (i.e., a microorchestrator), a network resource metadata database 330, a plurality ofnetwork resource domain managers 335 (including, but not limited to, aphysical network function (“PNF”) domain manager(s), a virtual networkfunction (“VNF”) domain manager(s), and/or the like), a plurality ofnetwork devices 340 (including, without limitation, devices 340 a-340 dthat each hosts at least one of one or more physical network resourcesor one or more virtual network resources, or the like), a networkresource telemetry repository 345, a compute resource orchestrator(s)350 (i.e., a micro orchestrator), a compute resource metadata database355, a plurality of compute resource domain managers 360 (including, butnot limited to, a compute domain manager(s) 360 a, a memory domainmanager(s) 360 b, a storage domain manager(s) 360 c, and/or the like), aplurality of compute resource pools 365 (including, without limitation,one or more compute pools 365 a (e.g., central processing unit (“CPU”)pools, graphics processing unit (“GPU”) pools, or the like), one or morememory pools 365 b (e.g., random access memory (“RAM”) pools, or thelike), one or more storage pools 365 c, or the like), a compute resourcetelemetry repository 370, and/or the like.

The macro orchestrator or business orchestrator 305 might automate,manage, or control each of the plurality of micro orchestrators (e.g.,network resource orchestrator(s) 325, compute resource orchestrator(s)350, billing resource orchestrator(s) (not shown), or orderorchestrator(s) (not shown), and/or the like), while each microorchestrator automates, manages, or controls at least one of a pluralityof domain managers (e.g., PNF domain manager(s) 335 a, VNF domainmanager(s) 335 b, compute domain manager(s) 360 a, memory domainmanager(s) 360 b, or storage domain manager(s) 360 c, and/or the like)or a plurality of network resources (e.g., network resources 140 of FIG.1, or the like) that may be located or hosted (physically or virtually)on devices 340 a-340 d, CPU or GPU pool(s) 365 a, RAM pool(s) 365 b, orstorage pool(s) 365 c or 365 d, and/or the like. For instance, a networkresource orchestrator 325 might automate, manage, and/or control one ormore of at least one PNF domain manager 335 a or at least one VNF domainmanager 335 b, while the at least one PNF domain manager 335 a mightautomate, manage, and/or control each of a plurality of physical networkresources located in devices 340 a and/or 340 b under its control, andthe at least one VNF domain manager 335 b might automate, manage, and/orcontrol each of a plurality of virtual network resources located indevices 340 c and/or 340 d under its control. Similarly, a computeresource orchestrator 350 might automate, manage, and/or control one ormore of at least one compute domain manager 360 a, at least one memorydomain manager 360 b, or at least one storage domain manager 360 c,while the at least one compute domain manager 360 a might automate,manage, and/or control each of a plurality of compute resources in oneor more compute pools (e.g., CPU and/or GPU pool(s) 365 a, or the like),and the at least one memory domain manager 360 b might automate, manage,and/or control each of a plurality of memory resources in one or morememory pools (e.g., RAM pool(s) 365 b, or the like), while the at leastone storage domain manager 360 c might automate, manage, and/or controleach of a plurality of storage resources in one or more storage pools365 c or 365 d, or the like.

In some embodiments, a billing resource orchestrator and an orderorchestrator might automate, manage, and/or control one or more of atleast one PNF domain manager 335 a or at least one VNF domain manager335 b, at least one compute domain manager 360 a, at least one memorydomain manager 360 b, at least one storage domain manager 360 c, or atleast one other domain manager (not shown), while the at least one PNFdomain manager 335 a might automate, manage, and/or control each of aplurality of physical network resources located in devices 340 a and/or340 b under its control, and the at least one VNF domain manager 335 bmight automate, manage, and/or control each of a plurality of virtualnetwork resources located in devices 340 c and/or 340 d under itscontrol, and while the at least one compute domain manager 360 a mightautomate, manage, and/or control each of a plurality of computeresources in one or more compute pools (e.g., CPU and/or GPU pool(s) 365a, or the like), and the at least one memory domain manager 360 b mightautomate, manage, and/or control each of a plurality of memory resourcesin one or more memory pools (e.g., RAM pool(s) 365 b, or the like),while the at least one storage domain manager 360 c might automate,manage, and/or control each of a plurality of storage resources in oneor more storage pools 365 c or 365 d, or the like, and while the atleast one other domain manager might automate, manage, and/or controlresources in its domain. The billing resource orchestrator mightautomate, manage, and/or control the domain managers within its domain,and ultimately the resources under the domain managers' domain, toperform automated billing-related tasks, while the order orchestratormight automate, manage, and/or control the domain managers within itsdomain, and ultimately the resources under the domain managers' domain,to perform automated order (or order-fulfillment) tasks.

In operation, a macro orchestrator (e.g., the business orchestrator 305,or the like) might receive, over a network (e.g., network(s) 115 and/or130 of FIG. 1, or the like), a request for network services from a userdevice (e.g., user device 310, or the like) associated with a customer(e.g., customer 315), the request for network services comprisingdesired characteristics and performance parameters for the requestednetwork services, without information regarding any of specifichardware, specific hardware type, specific location, or specific networkfor providing the requested network services. In some cases, the macroorchestrator might query a business information database (e.g., businessinformation database 320, or the like) to determine whether the requestfor network services falls under business parameters of the serviceprovider, whether the request for network services falls under a servicelevel agreement (“SLA”) between the customer and the service provider,and/or whether the request for network services is financially and/oroperationally feasible for the service provider, and/or the like. If nofor any of these determinations (where applicable), then the macroorchestrator might respond to the user device 310 with errors and/orrequests for additional information or authentication, or the like. Ifyes for each of these determinations (where applicable), then the macroorchestrator might initiate the multi-tiered orchestration andautomation, as described in detail below.

The macro orchestrator might send, to a first micro orchestrator among aplurality of micro orchestrators (e.g., at least one of network resourceorchestrator(s) 325 and/or compute resource orchestrator(s) 350), or thelike), the received request for network services. In response toreceiving the request for network services, the first micro orchestratormight identify one or more first network resources among a firstplurality of network resources for providing the requested networkservices, based at least in part on the desired characteristics andperformance parameters, and based at least in part on a determinationthat the one or more network resources are capable of providing networkservices having the desired characteristics and performance parameters.The first micro orchestrator might allocate at least one first networkresource among the identified one or more first network resources forproviding the requested network services.

In some embodiments, the first micro orchestrator might (continually,occasionally, randomly, or in response to a request for data, or thelike) receive, from one or more first domain managers among a firstplurality of domain managers in communication with the first microorchestrator, data regarding the first plurality of network resourcesthat are automated, managed, or controlled by each of the one or morefirst domain managers (where the data regarding the first plurality ofnetwork resources might be sent (continually, occasionally, randomly, orin response to a request for data, or the like) from the network devices(e.g., devices 340 a-340 d, or the like; as depicted by arrows fromdevices 340 a-340 d to PNF domain manager(s) 335 a or VNF domainmanager(s) 335 b in FIG. 3, or the like) and/or from compute resourcepools (e.g., CPU/GPU pool(s) 365 a, RAM pool(s) 365 b, or storagepool(s) 365 c, or the like; as depicted by arrows from CPU/GPU pool(s)365 a, RAM pool(s) 365 b, and storage pool(s) 365 c to compute domainmanager(s) 360 a, a memory domain manager(s) 360 b, and a storage domainmanager(s) 360 c in FIG. 3, or the like)). In such cases, identifying,with the first micro orchestrator, one or more first network resourcesamong a first plurality of network resources for providing the requestednetwork services might comprise identifying, with the first microorchestrator, one or more first network resources among a firstplurality of network resources for providing the requested networkservices, based at least in part on the data regarding the one or morefirst network resources, based at least in part on the desiredcharacteristics and performance parameters, and based at least in parton a determination that the one or more network resources are capable ofproviding network services having the desired characteristics andperformance parameters.

According to some embodiments, allocating, with the first microorchestrator, at least one first network resource among the identifiedone or more first network resources for providing the requested networkservices might comprise: sending, with the first micro orchestrator,commands to at least one first domain manager among the one or morefirst domain managers that automate, manage, or control the at least onefirst network resource; and in response to receiving the commands fromthe first micro orchestrator: determining, with the at least one firstdomain manager, an intent based at least in part on the desiredcharacteristics and performance parameters as comprised in the requestfor network services; generating and sending, with the at least onefirst domain manager, device language instructions for allocating the atleast one first network resource; and implementing, with the at leastone first domain manager, the at least one first network resource on theuser device associated with the customer, to provide the requestednetwork services.

In some embodiments, one of the macro orchestrator or the first microorchestrator might update a resource database (e.g., network resourcemetadata database(s) 330 and/or compute resource metadata database(s)355, or the like) with information indicating that the at least onefirst network resource has been allocated for providing the requestednetwork services and with information indicative of the desiredcharacteristics and performance parameters as comprised in the requestfor network services. According to some embodiments, network telemetrydata might be collected by the PNF domain manager(s) 335 a and/or theVNF domain manager(s) 335 b, which might store the network telemetrydata in network telemetry repository 345. Similarly, compute telemetrydata might be collected by the compute domain manager(s) 360 a, thememory domain manager(s) 360 b, and/or the storage domain manager(s) 360c, which might store the compute telemetry data in compute telemetryrepository 370.

In some cases, an audit engine (e.g., audit engine 170 of FIG. 1, or thelike) might determine whether each of the identified one or more firstnetwork resources conforms with the desired characteristics andperformance parameters, e.g., based on the collected network or computetelemetry data stored in network telemetry repository 345 or computetelemetry repository 370, respectively. In some instances, determiningwhether each of the identified one or more first network resourcesconforms with the desired characteristics and performance parametersmight comprise determining, with the audit engine, whether each of theidentified one or more first network resources conforms with the desiredcharacteristics and performance parameters on a periodic basis or inresponse to a request to perform an audit. Alternatively, oradditionally, determining whether each of the identified one or morefirst network resources conforms with the desired characteristics andperformance parameters might comprise determining, with the auditengine, whether each of the identified one or more first networkresources conforms with the desired characteristics and performanceparameters, by: measuring one or more network performance metrics ofeach of the identified one or more first network resources (as stored innetwork telemetry repository 345 or compute telemetry repository 370, orthe like); comparing, with the audit engine, the measured one or morenetwork performance metrics of each of the identified one or more firstnetwork resources with the desired performance parameters; determiningcharacteristics of each of the identified one or more first networkresources (in some cases, as stored in network telemetry repository 345or compute telemetry repository 370, or the like); and comparing, withthe audit engine, the determined characteristics of each of theidentified one or more first network resources with the desiredcharacteristics.

In such cases, each of the one or more network performance metrics mightinclude, without limitation, at least one of quality of service (“QoS”)measurement data, platform resource data and metrics, service usagedata, topology and reference data, historical network data, networkusage trend data, or one or more of information regarding at least oneof latency, jitter, bandwidth, packet loss, nodal connectivity, computeresources, storage resources, memory capacity, routing, operationssupport systems (“OSS”), or business support systems (“BSS”) orinformation regarding at least one of fault, configuration, accounting,performance, or security (“FCAPS”), and/or the like.

According to some embodiments, based on a determination that at leastone identified network resource among the identified one or more firstnetwork resources fails to conform with the desired performanceparameters within first predetermined thresholds or based on adetermination that the determined characteristics of the at least oneidentified network resource fails to conform with the desiredcharacteristics within second predetermined thresholds, the first microorchestrator either might reconfigure the at least one identifiednetwork resource to provide the desired characteristics and performanceparameters; or might reallocate at least one other identified networkresources among the identified one or more first network resources forproviding the requested network services.

FIGS. 4A-4D (collectively, “FIG. 4”) are flow diagrams illustrating amethod 400 for implementing intent-based multi-tiered orchestration andautomation, in accordance with various embodiments. Method 400 of FIG.4A continues onto FIG. 4C following the circular marker denoted, “A.”

While the techniques and procedures are depicted and/or described in acertain order for purposes of illustration, it should be appreciatedthat certain procedures may be reordered and/or omitted within the scopeof various embodiments. Moreover, while the method 400 illustrated byFIG. 4 can be implemented by or with (and, in some cases, are describedbelow with respect to) the systems, examples, or embodiments 100, 200,200′, and 300 of FIGS. 1, 2A, 2B, and 3, respectively (or componentsthereof), such methods may also be implemented using any suitablehardware (or software) implementation. Similarly, while each of thesystems, examples, or embodiments 100, 200, 200′, and 300 of FIGS. 1,2A, 2B, and 3, respectively (or components thereof), can operateaccording to the method 400 illustrated by FIG. 4 (e.g., by executinginstructions embodied on a computer readable medium), the systems,examples, or embodiments 100, 200, 200′, and 300 of FIGS. 1, 2A, 2B, and3 can each also operate according to other modes of operation and/orperform other suitable procedures.

In the non-limiting embodiment of FIG. 4A, method 400, at block 402,might comprise receiving, with a macro orchestrator over a network, arequest for network services from a user device associated with acustomer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services.

In some instances, the desired performance parameters might include,without limitation, at least one of a maximum latency, a maximum jitter,a maximum packet loss, or a maximum number of hops, and/or the like. Insome cases, the desired characteristics might include, but are notlimited to, at least one of requirement for network equipment to begeophysically proximate to the user device associated with the customer,requirement for network equipment to be located within a firstgeophysical location, requirement to avoid routing network trafficthrough a second geophysical location, requirement to route networktraffic through a third geophysical location, requirement to exclude afirst type of network resources from fulfillment of the requestednetwork services, requirement to include a second type of networkresources for fulfillment of the requested network services, requirementto fulfill the requested network services based on a single goalindicated by the customer, or requirement to fulfill the requestednetwork services based on multiple goals indicated by the customer,and/or the like.

At block 404, method 400 might comprise sending, with the macroorchestrator and to a first micro orchestrator among a plurality ofmicro orchestrators, the received request for network services. In somecases, the macro orchestrator might automate, manage, or control each ofthe plurality of micro orchestrators, while each micro orchestratormight automate, manage, or control at least one of a plurality of domainmanagers or a plurality of network resources. In some embodiments, themacro orchestrator and the plurality of micro orchestrators might eachinclude, without limitation, one of a server computer over a network, acloud-based computing system over a network, or a distributed computingsystem, and/or the like.

Method 400 might further comprise, at block 406, receiving, with thefirst micro orchestrator and from one or more first domain managersamong a first plurality of domain managers in communication with thefirst micro orchestrator, data regarding the first plurality of networkresources that are automated, managed, or controlled by each of the oneor more first domain managers.

Method 400 might further comprise receiving, with the first microorchestrator, the request for network services (block 408). At block410, method 400 might comprise, in response to receiving the request fornetwork services, identifying, with the first micro orchestrator, one ormore first network resources among a first plurality of networkresources for providing the requested network services, based at leastin part on the desired characteristics and performance parameters, basedat least in part on a determination that the one or more networkresources are capable of providing network services having the desiredcharacteristics and performance parameters, and, in some cases, based atleast in part on the data regarding the one or more first networkresources (received at block 406). In FIG. 4, although the process ofreceiving the data regarding the first plurality of network resources isshown to occur between sending the received request for network servicesto the first micro orchestrator and the first micro orchestratorreceiving the request for network services, the various embodiments arenot so limited, and the data regarding the first plurality of networkresources may be received by the first micro orchestrator continually,occasionally, randomly, or in response to a request for data, or thelike, as appropriate or as desired.

Method 400, at block 412, might comprise allocating, with the firstmicro orchestrator, at least one first network resource among theidentified one or more first network resources for providing therequested network services.

Method 400 might continue from FIG. 4A onto the process at block 424 inFIG. 4C following the circular marker denoted, “A.”

With reference to FIG. 4B, allocating, with the first microorchestrator, at least one first network resource among the identifiedone or more first network resources for providing the requested networkservices (at block 412) might comprise: sending, with the first microorchestrator, commands to at least one first domain manager among theone or more first domain managers that automate, manage, or control theat least one first network resource (block 414); and receiving, with theat least one first domain manager, the commands form the first microorchestrator (block 416). Method 400 might further comprise, in responseto receiving the commands from the first micro orchestrator:determining, with the at least one first domain manager, an intent basedat least in part on the desired characteristics and performanceparameters as comprised in the request for network services (block 418);generating and sending, with the at least one first domain manager,device language instructions for allocating the at least one firstnetwork resource (block 420); and implementing, with the at least onefirst domain manager, the at least one first network resource on theuser device associated with the customer, to provide the requestednetwork services (block 422).

At block 424 in FIG. 4C (following the circular marker denoted, “A,”from FIG. 4A), method 400 might comprise updating, with one of the macroorchestrator or the first micro orchestrator, a resource database withinformation indicating that the at least one first network resource hasbeen allocated for providing the requested network services and withinformation indicative of the desired characteristics and performanceparameters as comprised in the request for network services. At block426, method 400 might comprise determining, with an audit engine,whether each of the identified one or more first network resourcesconforms with the desired characteristics and performance parameters.

Method 400 might further comprise, at block 428, based on adetermination that at least one identified network resource among theidentified one or more first network resources fails to conform with thedesired performance parameters within first predetermined thresholds orbased on a determination that the determined characteristics of the atleast one identified network resource fails to conform with the desiredcharacteristics within second predetermined thresholds, performing oneof: reconfiguring, with the first micro orchestrator, the at least oneidentified network resource to provide the desired characteristics andperformance parameters (block 430); or reallocating, with the firstmicro orchestrator, at least one other identified network resource amongthe identified one or more first network resources for providing therequested network services (block 432).

Turning to FIG. 4D, determining whether each of the identified one ormore first network resources conforms with the desired characteristicsand performance parameters (at block 426) might comprise determining,with the audit engine, whether each of the identified one or more firstnetwork resources conforms with the desired characteristics andperformance parameters on a periodic basis or in response to a requestto perform an audit (block 434).

Alternatively, or additionally, FIG. 4D, determining whether each of theidentified one or more first network resources conforms with the desiredcharacteristics and performance parameters (at block 426) mightcomprise, at block 436, determining, with the audit engine, whether eachof the identified one or more first network resources conforms with thedesired characteristics and performance parameters, by: measuring one ormore network performance metrics of each of the identified one or morefirst network resources (block 438); comparing, with the audit engine,the measured one or more network performance metrics of each of theidentified one or more first network resources with the desiredperformance parameters (block 440); determining characteristics of eachof the identified one or more first network resources (block 442); andcomparing, with the audit engine, the determined characteristics of eachof the identified one or more first network resources with the desiredcharacteristics (block 444).

In some embodiments, each of the one or more network performance metricsmight include, but is not limited to, at least one of quality of service(“QoS”) measurement data, platform resource data and metrics, serviceusage data, topology and reference data, historical network data,network usage trend data, or one or more of information regarding atleast one of latency, jitter, bandwidth, packet loss, nodalconnectivity, compute resources, storage resources, memory capacity,routing, operations support systems (“OSS”), or business support systems(“BSS”) or information regarding at least one of fault, configuration,accounting, performance, or security (“FCAPS”), and/or the like.

FIG. 5A-5I (collectively, “FIG. 5”) is a schematic diagram illustratingexamples 500, 500′, and 500″ of various implementations for intent-basedorchestration using network parsimony trees, in accordance with variousembodiments. FIGS. 5A-5E depict non-limiting examples 500 of generalimplementation for intent-based orchestration using network parsimonytrees, while FIGS. 5F and 5G depict non-limiting examples 500′ ofembedded parsimony trees within portions of parsimony trees, and FIG. 5Hor 5I depicts non-limiting examples 500″ of use of filters to filter outone or more characteristics or one or more sub-characteristics and/or tochange the color temperature—in the case that color-codes embodied as acolor temperature or range of colors indicative of characteristics orperformance parameters including one or more of latency, jitter, packetloss, number of hops, bandwidth, utilization, capacity, or proximity,and/or the like, are used for portions of parsimony trees—based onchanges in measured network metrics. In the non-limiting embodiments ofFIG. 5, one or more parsimony trees might be generated, based on networktelemetry data of one or more networks, where each parsimony tree mightbe a graphical representation of characteristics and performanceparameters based on the network telemetry data of the one or morenetworks, and the system might perform network orchestration andautomation based on the generated one or more parsimony trees.

With reference to the non-limiting embodiment of FIGS. 5A-5C, as shownin FIG. 5A, a computing system 505 might receive, over a network, arequest 510 for network services from a user device associated with acustomer, the request 510 for network services comprising desiredcharacteristics and performance parameters 515 for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services. In some embodiments, thedesired characteristics and performance parameters 515 might include,without limitation, at least one of a maximum latency, a maximum jitter,a maximum packet loss, a maximum cost, a maximum number of hops,requirement for network equipment to be geophysically proximate to theuser device associated with the customer, requirement for networkequipment to be located within a first geophysical location, requirementto avoid routing network traffic through a second geophysical location,requirement to route network traffic through a third geophysicallocation, requirement to exclude a first type of network resources fromfulfillment of the requested network services, requirement to include asecond type of network resources for fulfillment of the requestednetwork services, requirement to fulfill the requested network servicesbased on a single goal indicated by the customer, or requirement tofulfill the requested network services based on multiple goals indicatedby the customer, and/or the like.

In response to receiving the request 510 for network services, thecomputing system 505 might generate a first request-based parsimony tree520 based at least in part on the desired characteristics andperformance parameters contained in the request for network services.According to some embodiments, the first request-based parsimony tree520 might be a graphical representation including, without limitation,an end-point 525 a of a first portion 525 representing delivery locationof the requested network services (in this case, “Site Z,” which mightbe a customer premises associated with the customer or with the userdevice associated with the customer, or the like), an endpoint 530 a ofeach of one or more second portions 530 that connect with the firstportion 525 representing a service provider site, each intersection 535between two or more second portions or between the first portion and oneof the second portions representing a network resource node, andcharacteristics of the first and second portions representing thedesired characteristics and performance parameters contained in therequest for network services, and/or the like.

Turning to FIG. 5B, the computing system 505 might access, from adatastore, a plurality of first network-based parsimony trees 545 a-545n (collectively, “first network-based parsimony trees 545,” or thelike), each of the plurality of first network-based parsimony trees 545being generated based on measured network metrics 540. In someembodiments, each first network-based parsimony tree 545 might be agraphical representation including, but not limited to, an end-point 550a of a third portion 550 representing the delivery location (in thiscase, “Site Z,” which might be a customer premises associated with thecustomer or with the user device associated with the customer, or thelike) of the requested network services, an endpoint 555 a of each ofone or more fourth portions 555 that connect with the third portionrepresenting a service provider site (in this case, one of Sites Athrough D, or the like), each intersection 560 between two or morefourth portions or between the third portion and one of the fourthportions representing a network resource node, and characteristics ofthe third and fourth portions representing measured characteristics andperformance parameters based on the measured network metrics.

According to some embodiments, the first portion 525 of the firstrequest-based parsimony tree 520 and the third portion 550 of each firstnetwork-based parsimony tree 545 might each be represented by a trunk,while the one or more second portions 530 of the first request-basedparsimony tree 520 and the one or more fourth portions 555 of each firstnetwork-based parsimony tree 545 might each be represented by a branch,and, in each parsimony tree 520 or 545, one or more branches mightconnect with the trunk. In some cases, in each of at least one parsimonytree, two or more branches might connect with each other via one or moreconnector branches and via the trunk, or the like.

In some embodiments, the characteristics of the first and secondportions and the characteristics of the third and fourth portions mightinclude, without limitation, at least one of thickness of each portion,length of each portion from the delivery location of the requestednetwork services, number of network resource nodes on each portion,color-code of each portion, number of second or fourth portions, angleof each second or fourth portion relative to the first or third portion,number of any connector portions between two or more second or fourthportions, relative location of any connector portions between two ormore second or fourth portions, length of any connector portions betweentwo or more second or fourth portions, or angle of any connectorportions between two or more second or fourth portions, and/or the like.In some instances, the characteristics of the first and second portionsand the characteristics of the third and fourth portions might representone or more of latency, jitter, packet loss, number of hops, bandwidth,utilization, capacity, or proximity, and/or the like.

According to some embodiments, as shown in FIG. 5C, the first computingsystem 505 might compare the first request-based parsimony tree 520 withone or more first network-based parsimony trees (e.g., firstnetwork-based parsimony tree 545 a, or the like) among the plurality offirst network-based parsimony trees 545 to determine a fitness score (inthis case, 560 a) for each first network-based parsimony tree 545.Although FIG. 5C depicts comparison with one network-based parsimonytree 545 a, the various embodiments are not so limited, and any suitablenumber (if not all) of the plurality of network-based parsimony trees545 a-545 n may be compared with the first request-based parsimony tree520, resulting in a fitness score 560 for each network-based parsimonytree 545 (e.g., a fitness score 560 a for network-based parsimony tree545 a, a fitness score 560 b for network-based parsimony tree 545 b, andso on). In some instances, each fitness score 560 might be a valueindicative of a level of heuristic matching (in some cases, embodied asa percentage match) between the first request-based parsimony tree 520with one of the one or more first network-based parsimony trees 545. Insome embodiments, comparing the first request-based parsimony tree 520with one or more first network-based parsimony trees 545 might comprisecomparing the first request-based parsimony tree 520 with one or morefirst network-based parsimony trees 545 using one or more GPUs, or thelike.

Merely by way of example, in some cases, the first computing system 505might identify a best-fit network-based parsimony tree among the one ormore first network-based parsimony trees 545 based on the fitness scores560 of the one or more first network-based parsimony trees; mightidentify one or more first network resources among a first plurality ofnetwork resources for providing the requested network services, based atleast in part on network resources represented within the identifiedbest-fit network-based parsimony tree; and might allocate at least onefirst network resource among the identified one or more first networkresources for providing the requested network services. According tosome embodiments, identifying the best-fit network-based parsimony treemight comprise identifying the most parsimonious first network-basedparsimony tree for providing the requested network resources. That is,the first computing system 505 might identify the tree with the simplest(or least complicated) network characteristics or the tree with theshortest (or fewest) network routing requirements, or the like, thatenables allocation of the requested network services with the desiredcharacteristics and performance parameters. In some embodiments, atleast one of generating first network-based parsimony trees, comparingthe first request-based parsimony tree with the one or more firstnetwork-based parsimony tree, identifying the best-fit network-basedparsimony tree, or identifying the one or more first network resourcesmay be performed using one or more of at least one ML system, at leastone AI systems, or at least one NN system, and/or the like.

According to some embodiments, the first computing system 505 mightreceive updated measured network metrics; might, in response toreceiving the updated measured network metrics, generate a plurality ofupdated first network-based parsimony trees; and might replace theplurality of first network-based parsimony trees in the datastore withthe plurality of updated first network-based parsimony trees. In someembodiments, the updated measured network metrics might be receivedaccording to one of the following: on a periodic basis, on a continualbasis, on a random basis, or in response to a change in networkcharacteristic or performance in at least one network resource in anetwork, and/or the like. In some cases, each of the plurality ofupdated first network-based parsimony trees might be stored in thedatastore as an image file (e.g., .jpg file, .tiff file, .gif file, .bmpfile, .png file, .dwf file, .dwg file, .drw file, .stl file, .pdf file,.svg file, .cgm file, etc.).

In some embodiments, rather than a single request-based parsimony treebeing generated in response to receiving the request for networkservices, the first computing system 505 might generate a plurality offirst request-based parsimony trees, each representing a desiredcharacteristic or performance parameter, and the subsequent functionsperformed by the first orchestrator might be performed on this pluralityof first request-based parsimony trees rather than the singlerequest-based parsimony tree.

FIGS. 5D and 5E depict non-limiting examples of network parsimony treesthat may correspond to one or more first network-based parsimony trees545 a-545 h that collectively show differences in thickness of linescorresponding to the third and fourth portions 550 and 555, thedifferences in angles between each fourth portion 555 and correspondingthird portion 550, differences in angles between connecting portionsbetween two fourth portions 555 (where such connecting portions mightrepresent ring network configurations or the like), the differences inthe number of connecting portions between two fourth portion 555, thedifferences in terms of whether the third portion extends past theintersection with the first of the fourth portions, the differences interms of whether particular fourth portions extend past thecorresponding intersections with the third portion, the differences interms of whether a connecting portion extends beyond one or both of thefourth portions that it connects, the differences in the length of thethird portion, the differences in the length of each correspondingfourth portion, the differences in terms of the number of fourthportions connected to the third portion, the differences in terms ofwhether a connecting portion connects more than two fourth portions,and/or the like. Although particular examples of first network-basedparsimony trees 545 a-545 h are shown in FIGS. 5D and 5E, the variousembodiments are not so limited, and any suitable network-based parsimonytrees may be used or generated having any suitable number or type ofcharacteristics, including, but not limited to, at least one ofthickness of each portion, length of each portion from the deliverylocation of the requested network services, number of network resourcenodes on each portion, color-code of each portion, number of fourthportions, angle of each fourth portion relative to the third portion,number of any connector portions between two or more fourth portions,relative location of any connector portions between two or more fourthportions, length of any connector portions between two or more fourthportions, or angle of any connector portions between two or more fourthportions, and/or the like. Such characteristics of the third and fourthportions may represent one or more of latency, jitter, packet loss,number of hops, bandwidth, utilization, capacity, or proximity, and/orthe like. Similar numbers or types of characteristics may also apply togeneration of the request-based parsimony tree(s). In some cases,thickness of lines representing portions of each parsimony tree might beindicative of bandwidth, utilization, and/or capacity, while length ofsuch lines might be indicative of proximity, and/or the like, althoughthe various embodiments are not so limited, and the thickness and lengthmay be used to represent other characteristics, while other features ofthe lines may represent bandwidth, utilization, capacity, and/orproximity.

With reference to the non-limiting embodiment 500′ of FIG. 5F, in someinstances, each portion 550 or 555 of each first network-based parsimonytree 545 might be represented by a second network-based parsimony tree(e.g., one of second network-based parsimony trees 565 a-565 z, or thelike) among a plurality of second network-based parsimony tree 565 thatis indicative of characteristics and performance parameters of thatportion, where each portion of each second network-based parsimony tree565 might be represented by a third network-based parsimony tree 570among a plurality of third network-based parsimony tree 570 that isindicative of characteristics and performance parameters of thatportion, and so on in a fractal-like manner.

Turning to the non-limiting embodiment 500′ of FIG. 5G, alternative, oradditional, to the fractal-like embedding of network-based parsimonytree within portions of network-based parsimony trees, eachnetwork-based parsimony tree or portions thereof may have embeddedtherein one or more network-characteristic parsimony trees 575,including, but not limited to, parsimony trees that are representativeof latency (e.g., parsimony tree 575 a, or the like), parsimony treesthat are representative of cost (e.g., parsimony tree 575 b, or thelike), parsimony trees that are representative of jitter (e.g.,parsimony tree 575 c, or the like), parsimony trees that arerepresentative of packet loss (e.g., parsimony tree 575 d, or the like),and so on.

Referring to the non-limiting embodiment 500″ of FIG. 5H, in someembodiments, the first computing system 505 might apply a first filter580 to at least one first network-based parsimony tree (e.g., firstnetwork-based parsimony tree 545 a, or the like) among the one or morefirst network-based parsimony trees 545 to filter out one or morecharacteristics or one or more sub-characteristics, in some cases,represented by network-characteristic parsimony trees 585. For example,the first filter 580 might filter first network-based parsimony tree 545a to produce one or more of network-characteristic parsimony trees 585 a(representative of latency), network-characteristic parsimony trees 585b (representative of cost), network-characteristic parsimony trees 585 c(representative of jitter), or network-characteristic parsimony trees585 d (representative of packet loss), and/or the like. And suchfiltering may be performed prior to comparing the first request-basedparsimony tree with the one or more first network-based parsimony trees,as shown in FIG. 5C, or the like.

According to some embodiments, the characteristics of the third andfourth portions might include color-codes embodied as a colortemperature or range of colors for each portion or for each parsimonytree that is indicative of characteristics or performance parametersincluding one or more of latency, jitter, packet loss, number of hops,bandwidth, utilization, capacity, or proximity, and/or the like. In suchcases, alternative or additional to applying the first filter, the firstcomputing system 505 might apply a second filter 590 to at least onefirst network-based parsimony tree 595 a among the one or more firstnetwork-based parsimony trees to change the color temperature based onchanges in measured network metrics to produce at least one fourthnetwork-based parsimony tree 595 b. The second filter 590 might performone of the following: (i) apply a static filter that passes particularcolors (which might represent characteristics of the network); (ii)apply a static filter that blocks particular colors; (iii) apply adynamic filter that passes a particular shift in color temperature(thereby apply the same amount of shift for each color, as shown in FIG.5I); (iv) apply a dynamic filter that blocks a particular shift in colortemperature; and/or the like). In some embodiments, the computing system505, in some cases aided by at least one of machine learning, AI, and/orneural network functionalities, may apply the first filter 580 and/orthe second filter 590 as necessary or as appropriate to ensure optimizedintent-based orchestration and automation.

FIGS. 6A-6E (collectively, “FIG. 6”) are flow diagrams illustrating amethod 600 for implementing intent-based orchestration using networkparsimony trees, in accordance with various embodiments. Method 600 ofFIG. 6A, 6D, or 6E continues onto FIG. 6B following the circular markerdenoted, “A,” and returns to FIG. 6A, 6D, or 6E following the circularmarker denoted, “B.” Method 600 of FIG. 6A, 6D, or 6E continues ontoFIG. 6C following the circular marker denoted, “C,” and returns to FIG.6A, 6D, or 6E following the circular marker denoted, “D.”

While the techniques and procedures are depicted and/or described in acertain order for purposes of illustration, it should be appreciatedthat certain procedures may be reordered and/or omitted within the scopeof various embodiments. Moreover, while the method 600 illustrated byFIG. 6 can be implemented by or with (and, in some cases, are describedbelow with respect to) the systems, examples, or embodiments 100, 200,200′, 300, 500, 500′, and 500″ of FIGS. 1, 2A, 2B, 3, 5A-5E, 5F-5G, and5H-5I, respectively (or components thereof), such methods may also beimplemented using any suitable hardware (or software) implementation.Similarly, while each of the systems, examples, or embodiments 100, 200,200′, 300, 500, 500′, and 500″ of FIGS. 1, 2A, 2B, 3, 5A-5E, 5F-5G, and5H-5I, respectively (or components thereof), can operate according tothe method 600 illustrated by FIG. 6 (e.g., by executing instructionsembodied on a computer readable medium), the systems, examples, orembodiments 100, 200, 200′, 300, 500, 500′, and 500″ of FIGS. 1, 2A, 2B,3, 5A-5E, 5F-5G, and 5H-5I can each also operate according to othermodes of operation and/or perform other suitable procedures.

In the non-limiting embodiment of FIG. 6A, method 600, at block 605,might comprise receiving, with a computing system, a request for networkservices from a user device associated with a customer, the request fornetwork services comprising desired characteristics and performanceparameters for the requested network services, without informationregarding any of specific hardware, specific hardware type, specificlocation, or specific network for providing the requested networkservices.

In some embodiments, the computing system might include, but is notlimited to, one of a server computer over a network, one or moregraphics processing units (“GPUs”), a cloud-based computing system overa network, or a distributed computing system, and/or the like. In someinstances, the desired performance parameters might include, withoutlimitation, at least one of a maximum latency, a maximum jitter, amaximum packet loss, or a maximum number of hops, and/or the like. Insome cases, the desired characteristics might include, but are notlimited to, at least one of requirement for network equipment to begeophysically proximate to the user device associated with the customer,requirement for network equipment to be located within a firstgeophysical location, requirement to avoid routing network trafficthrough a second geophysical location, requirement to route networktraffic through a third geophysical location, requirement to exclude afirst type of network resources from fulfillment of the requestednetwork services, requirement to include a second type of networkresources for fulfillment of the requested network services, requirementto fulfill the requested network services based on a single goalindicated by the customer, or requirement to fulfill the requestednetwork services based on multiple goals indicated by the customer,and/or the like.

At block 610, method 600 might comprise, in response to receiving therequest for network services, generating, with the computing system, afirst request-based parsimony tree based at least in part on the desiredcharacteristics and performance parameters contained in the request fornetwork services. According to some embodiments, the first request-basedparsimony tree might be a graphical representation including, withoutlimitation, an end-point of a first portion representing deliverylocation of the requested network services, an endpoint of each of oneor more second portions that connect with the first portion representinga service provider site, each intersection between two or more secondportions or between the first portion and one of the second portionsrepresenting a network resource node, and characteristics of the firstand second portions representing the desired characteristics andperformance parameters contained in the request for network services,and/or the like.

Method 600 might further comprise, at block 615, accessing, with thecomputing system and from a datastore, a plurality of firstnetwork-based parsimony trees, each of the plurality of firstnetwork-based parsimony trees being generated based on measured networkmetrics. In some embodiments, each first network-based parsimony treemight be a graphical representation including, but not limited to, anend-point of a third portion representing the delivery location of therequested network services, an endpoint of each of one or more fourthportions that connect with the third portion representing a serviceprovider site, each intersection between two or more fourth portions orbetween the third portion and one of the fourth portions representing anetwork resource node, and characteristics of the third and fourthportions representing measured characteristics and performanceparameters based on the measured network metrics.

According to some embodiments, the first portion of the firstrequest-based parsimony tree and the third portion of each firstnetwork-based parsimony tree might each be represented by a trunk, whilethe one or more second portions of the first request-based parsimonytree and the one or more fourth portions of each first network-basedparsimony tree might each be represented by a branch, and, in eachparsimony tree, one or more branches might connect with the trunk. Insome cases, in each of at least one parsimony tree, two or more branchesmight connect with each other via one or more connector branches and viathe trunk, or the like. In some instances, each portion of each firstnetwork-based parsimony tree might be represented by a secondnetwork-based parsimony tree among a plurality of second network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion, where each portion of each secondnetwork-based parsimony tree might be represented by a thirdnetwork-based parsimony tree among a plurality of third network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion, and so on in a fractal-like manner.

In some embodiments, the characteristics of the first and secondportions and the characteristics of the third and fourth portions mightinclude, without limitation, at least one of thickness of each portion,length of each portion from the delivery location of the requestednetwork services, number of network resource nodes on each portion,color-code of each portion, number of second or fourth portions, angleof each second or fourth portion relative to the first or third portion,number of any connector portions between two or more second or fourthportions, relative location of any connector portions between two ormore second or fourth portions, length of any connector portions betweentwo or more second or fourth portions, or angle of any connectorportions between two or more second or fourth portions, and/or the like.In some instances, the characteristics of the first and second portionsand the characteristics of the third and fourth portions might representone or more of latency, jitter, packet loss, number of hops, bandwidth,utilization, capacity, or proximity, and/or the like.

Method 600 either might continue onto the process at block 620 or mightcontinue onto the process at block 640 or block 645 in FIG. 6B,following the circular marker denoted, “A.”

At block 640 in FIG. 6B (following the circular marker denoted, “A”),method 600 might comprise applying a first filter to at least one firstnetwork-based parsimony tree among the one or more first network-basedparsimony trees to filter out one or more characteristics or one or moresub-characteristics, prior to comparing the first request-basedparsimony tree with the one or more first network-based parsimony trees.Method 600 might continue onto the process at block 620 in FIG. 6A,following the circular marker denoted, “B.”

According to some embodiments, the characteristics of the third andfourth portions might include color-codes embodied as a colortemperature or range of colors for each portion or for each parsimonytree that is indicative of characteristics or performance parametersincluding one or more of latency, jitter, packet loss, number of hops,bandwidth, utilization, capacity, or proximity, and/or the like. In suchcases, alternative to the process at block 640, method 600, at block 645in FIG. 6B (following the circular marker denoted, “A”), might compriseapplying a second filter to at least one first network-based parsimonytree among the one or more first network-based parsimony trees to changethe color temperature based on changes in measured network metrics.Method 600 might continue onto the process at block 620 in FIG. 6A,following the circular marker denoted, “B.”

At block 620 in FIG. 6A (either continuing from the process at block 615or following the circular marker denoted, “B,” from the process at block640 or at block 645 in FIG. 6B), method 600 might comprise comparing,with the computing system, the first request-based parsimony tree withone or more first network-based parsimony trees among the plurality offirst network-based parsimony trees to determine a fitness score foreach first network-based parsimony tree. In some instances, each fitnessscore might be a value indicative of a level of heuristic matching (insome cases, embodied as a percentage match) between the firstrequest-based parsimony tree with one of the one or more firstnetwork-based parsimony trees. In some embodiments, comparing the firstrequest-based parsimony tree with one or more first network-basedparsimony trees might comprise comparing the first request-basedparsimony tree with one or more first network-based parsimony treesusing one or more graphics processing units (“GPUs”), or the like. GPUsare ideal for such comparison work, as a single GPU is analogous to anarmy of millions of kindergarteners, and are thus more suited to tryingto perform tasks analogous to fitting a million different shaped pegsinto a million different shaped holes, whereas a central processing unit(“CPU”) is analogous to a person with a PhD per core (with each CPUhaving multiple cores (e.g., 12 cores per machine, or the like)) and isthus suitable for solving complex mathematical problems.

Method 600 might further comprise identifying, with the computingsystem, a best-fit network-based parsimony tree among the one or morefirst network-based parsimony trees based on the fitness scores of theone or more first network-based parsimony trees (block 625);identifying, with the computing system, one or more first networkresources among a first plurality of network resources for providing therequested network services, based at least in part on network resourcesrepresented within the identified best-fit network-based parsimony tree(block 630); and allocating, with the computing system, at least onefirst network resource among the identified one or more first networkresources for providing the requested network services (block 635).According to some embodiments, identifying the best-fit network-basedparsimony tree might comprise identifying the most parsimonious firstnetwork-based parsimony tree for providing the requested networkresources. That is, the computing system might identify the tree withthe simplest (or least complicated) network characteristics or the treewith the shortest (or fewest) network routing requirements, or the like,that enables allocation of the requested network services with thedesired characteristics and performance parameters. In some embodiments,at least one of generating first network-based parsimony trees,comparing the first request-based parsimony tree with the one or morefirst network-based parsimony tree, identifying the best-fitnetwork-based parsimony tree, or identifying the one or more firstnetwork resources may be performed using one or more of at least onemachine learning (“ML”) system, at least one artificial intelligence(“AI”) systems, or at least one neural network (“NN”) system, and/or thelike.

Method 600 might continue onto the process at block 650 in FIG. 6C,following the circular marker denoted, “C.”

At block 650 in FIG. 6C (following the circular marker denoted, “C”),method 600 might comprise receiving updated measured network metrics.Method 600 might further comprise, in response to receiving the updatedmeasured network metrics, generating a plurality of updated firstnetwork-based parsimony trees (block 655), and replacing the pluralityof first network-based parsimony trees in the datastore with theplurality of updated first network-based parsimony trees (block 660).According to some embodiments, the updated measured network metricsmight be received according to one of the following: on a periodicbasis, on a continual basis, on a random basis, or in response to achange in network characteristic or performance in at least one networkresource in a network, and/or the like. In some cases, each of theplurality of updated first network-based parsimony trees might be storedin the datastore as an image file (e.g., .jpg file, .tiff file, .giffile, .bmp file, .png file, .dwf file, .dwg file, .drw file, .stl file,.pdf file, .svg file, .cgm file, etc.). Method 600 might return to theprocess at block 615 in FIG. 6A, following the circular marker denoted,“D.”

Alternative to the embodiment of FIG. 6A, in the non-limiting embodimentof FIG. 6D, method 600, at block 665, might comprise receiving, with acomputing system, a request for network services from a user deviceassociated with a customer, the request for network services comprisingdesired characteristics and performance parameters for the requestednetwork services, without information regarding any of specifichardware, specific hardware type, specific location, or specific networkfor providing the requested network services.

In some embodiments, like in the embodiment of FIG. 6A, the computingsystem might include, but is not limited to, one of a server computerover a network, one or more graphics processing units (“GPUs”), acloud-based computing system over a network, or a distributed computingsystem, and/or the like. In some instances, the desired performanceparameters might include, without limitation, at least one of a maximumlatency, a maximum jitter, a maximum packet loss, or a maximum number ofhops, and/or the like. In some cases, the desired characteristics mightinclude, but are not limited to, at least one of requirement for networkequipment to be geophysically proximate to the user device associatedwith the customer, requirement for network equipment to be locatedwithin a first geophysical location, requirement to avoid routingnetwork traffic through a second geophysical location, requirement toroute network traffic through a third geophysical location, requirementto exclude a first type of network resources from fulfillment of therequested network services, requirement to include a second type ofnetwork resources for fulfillment of the requested network services,requirement to fulfill the requested network services based on a singlegoal indicated by the customer, or requirement to fulfill the requestednetwork services based on multiple goals indicated by the customer,and/or the like.

At block 670, method 600 might comprise, in response to receiving therequest for network services, generating, with the computing system, aplurality of first request-based parsimony trees based at least in parton the desired characteristics and performance parameters contained inthe request for network services. According to some embodiments, eachfirst request-based parsimony tree among the plurality of firstrequest-based parsimony trees might represent a desired characteristicor performance parameter, or the like.

Method 600 might further comprise, at block 675, accessing, with thecomputing system and from a datastore, a plurality of firstnetwork-based parsimony trees, each of the plurality of firstnetwork-based parsimony trees being generated based on measured networkmetrics. In some embodiments, each first network-based parsimony treemight correspond to each of the desired characteristics and performanceparameters, each of the plurality of first network-based parsimony treesbeing generated based on measured network metrics (or network telemetrydata, or the like).

According to some embodiments, like in the embodiment of FIG. 6A, thefirst portion of each first request-based parsimony tree and the thirdportion of each first network-based parsimony tree might each berepresented by a trunk, while the one or more second portions of eachfirst request-based parsimony tree and the one or more fourth portionsof each first network-based parsimony tree might each be represented bya branch, and, in each parsimony tree, one or more branches mightconnect with the trunk. In some cases, in each of at least one parsimonytree, two or more branches might connect with each other via one or moreconnector branches and via the trunk, or the like. In some instances,each portion of each first network-based parsimony tree might berepresented by a second network-based parsimony tree among a pluralityof second network-based parsimony tree that is indicative ofcharacteristics and performance parameters of that portion, where eachportion of each second network-based parsimony tree might be representedby a third network-based parsimony tree among a plurality of thirdnetwork-based parsimony tree that is indicative of characteristics andperformance parameters of that portion, and so on in a fractal-likemanner.

In some embodiments, the characteristics of the first and secondportions and the characteristics of the third and fourth portions mightinclude, without limitation, at least one of thickness of each portion,length of each portion from the delivery location of the requestednetwork services, number of network resource nodes on each portion,color-code of each portion, number of second or fourth portions, angleof each second or fourth portion relative to the first or third portion,number of any connector portions between two or more second or fourthportions, relative location of any connector portions between two ormore second or fourth portions, length of any connector portions betweentwo or more second or fourth portions, or angle of any connectorportions between two or more second or fourth portions, and/or the like.In some instances, the characteristics of the first and second portionsand the characteristics of the third and fourth portions might representone or more of latency, jitter, packet loss, number of hops, bandwidth,utilization, capacity, or proximity, and/or the like.

Method 600 either might continue onto the process at block 680 or mightcontinue onto the process at block 640 or block 645 in FIG. 6B,following the circular marker denoted, “A.” The processes at blocks 640and 645 may be as described in detail above, where after the process atblock 640 or at block 645, method 600 might continue onto the process atblock 680 in FIG. 6D, following the circular marker denoted, “B.”

At block 680 in FIG. 6D (either continuing from the process at block 675or following the circular marker denoted, “B,” from the process at block640 or at block 645 in FIG. 6B), method 600 might comprise comparing,with the computing system, each first request-based parsimony treerepresenting one of the desired characteristics and performanceparameters with a corresponding plurality of first network-basedparsimony trees to determine a fitness score for each firstnetwork-based parsimony tree. In some instances, as in the embodiment ofFIG. 6A, each fitness score might be a value indicative of a level ofheuristic matching (in some cases, embodied as a percentage match)between each first request-based parsimony tree with one of the one ormore first network-based parsimony trees. In some embodiments, comparingeach first request-based parsimony tree with one or more firstnetwork-based parsimony trees might comprise comparing each firstrequest-based parsimony tree with one or more first network-basedparsimony trees using one or more GPUs, or the like.

Method 600 might further comprise identifying, with the computingsystem, the best-fit network-based parsimony tree corresponding to eachof the desired characteristics and performance parameters based on thefitness scores of the one or more first network-based parsimony trees(block 685); identifying, with the computing system, one or more firstnetwork resources among a first plurality of network resources forproviding the requested network services, based at least in part on thenetwork resources represented within the identified best-fitnetwork-based parsimony trees (block 690); and allocating, with thecomputing system, at least one first network resource among theidentified one or more first network resources for providing therequested network services (block 695). According to some embodiments,identifying the one or more best-fit network-based parsimony tree mightcomprise identifying the one or more most parsimonious firstnetwork-based parsimony tree for providing the requested networkresources. In some embodiments, at least one of generating the pluralityof first network-based parsimony trees, comparing each firstrequest-based parsimony tree representing one of the desiredcharacteristics and performance parameters with a correspondingplurality of first network-based parsimony trees, identifying thebest-fit network-based parsimony tree, or identifying the one or morefirst network resources may be performed using one or more of at leastone ML system, at least one AI systems, or at least one NN system,and/or the like.

Method 600 might continue onto the process at block 650 in FIG. 6C,following the circular marker denoted, “C.” The processes at blocks650-660 in FIG. 6C (following the circular marker denoted, “C”) may beas described in detail above, where after the process at block 660,method 600 might return to the process at block 675 in FIG. 6D,following the circular marker denoted, “D.”

Alternative to each of the embodiment of FIG. 6A or the embodiment ofFIG. 6D, in the non-limiting embodiment of FIG. 6E, method 600, at block605′, might comprise receiving, with a macro orchestrator over anetwork, a request for network services from a user device associatedwith a customer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services.

In some embodiments, the macro orchestrator might include, withoutlimitation, one of a server computer over a network, a cloud-basedcomputing system over a network, or a distributed computing system,and/or the like. In some instances, the desired performance parametersmight include, without limitation, at least one of a maximum latency, amaximum jitter, a maximum packet loss, or a maximum number of hops,and/or the like. In some cases, the desired characteristics mightinclude, but are not limited to, at least one of requirement for networkequipment to be geophysically proximate to the user device associatedwith the customer, requirement for network equipment to be locatedwithin a first geophysical location, requirement to avoid routingnetwork traffic through a second geophysical location, requirement toroute network traffic through a third geophysical location, requirementto exclude a first type of network resources from fulfillment of therequested network services, requirement to include a second type ofnetwork resources for fulfillment of the requested network services,requirement to fulfill the requested network services based on a singlegoal indicated by the customer, or requirement to fulfill the requestednetwork services based on multiple goals indicated by the customer,and/or the like.

At block 610′, method 600 might comprise, in response to receiving therequest for network services, generating, with a first microorchestrator among a plurality of micro orchestrators, a firstrequest-based parsimony tree based at least in part on the desiredcharacteristics and performance parameters contained in the request fornetwork services. According to some embodiments, the first request-basedparsimony tree might be a graphical representation including, withoutlimitation, an end-point of a first portion representing deliverylocation of the requested network services, an endpoint of each of oneor more second portions that connect with the first portion representinga service provider site, each intersection between two or more secondportions or between the first portion and one of the second portionsrepresenting a network resource node, and characteristics of the firstand second portions representing the desired characteristics andperformance parameters contained in the request for network services,and/or the like. In some cases, the plurality of micro orchestratorsmight each include, but is not limited to, one of a server computer overa network, a cloud-based computing system over a network, or adistributed computing system, and/or the like. The macro orchestratorand the plurality of micro orchestrators may be similar, if notidentical, to the macro orchestrator 105, 205, 250, and/or 305 and theplurality of micro orchestrators 110, 210, 255, 325, and/or 350 ofsystems 100, 200, 200′, and 300 of FIGS. 1, 2A, 2B, and 3, as describedin detail above, at least in terms of structure and/or function.

Method 600 might further comprise, at block 615′, accessing, with thefirst micro orchestrator and from a datastore, a plurality of firstnetwork-based parsimony trees, each of the plurality of firstnetwork-based parsimony trees being generated based on measured networkmetrics. In some embodiments, each first network-based parsimony treemight be a graphical representation including, but not limited to, anend-point of a third portion representing the delivery location of therequested network services, an endpoint of each of one or more fourthportions that connect with the third portion representing a serviceprovider site, each intersection between two or more fourth portions orbetween the third portion and one of the fourth portions representing anetwork resource node, and characteristics of the third and fourthportions representing measured characteristics and performanceparameters based on the measured network metrics.

According to some embodiments, like in the embodiment of FIG. 6A, thefirst portion of the first request-based parsimony tree and the thirdportion of each first network-based parsimony tree might each berepresented by a trunk, while the one or more second portions of thefirst request-based parsimony tree and the one or more fourth portionsof each first network-based parsimony tree might each be represented bya branch, and, in each parsimony tree, one or more branches mightconnect with the trunk. In some cases, in each of at least one parsimonytree, two or more branches might connect with each other via one or moreconnector branches and via the trunk, or the like. In some instances,each portion of each first network-based parsimony tree might berepresented by a second network-based parsimony tree among a pluralityof second network-based parsimony tree that is indicative ofcharacteristics and performance parameters of that portion, where eachportion of each second network-based parsimony tree might be representedby a third network-based parsimony tree among a plurality of thirdnetwork-based parsimony tree that is indicative of characteristics andperformance parameters of that portion, and so on in a fractal-likemanner.

In some embodiments, the characteristics of the first and secondportions and the characteristics of the third and fourth portions mightinclude, without limitation, at least one of thickness of each portion,length of each portion from the delivery location of the requestednetwork services, number of network resource nodes on each portion,color-code of each portion, number of second or fourth portions, angleof each second or fourth portion relative to the first or third portion,number of any connector portions between two or more second or fourthportions, relative location of any connector portions between two ormore second or fourth portions, length of any connector portions betweentwo or more second or fourth portions, or angle of any connectorportions between two or more second or fourth portions, and/or the like.In some instances, the characteristics of the first and second portionsand the characteristics of the third and fourth portions might representone or more of latency, jitter, packet loss, number of hops, bandwidth,utilization, capacity, or proximity, and/or the like.

Method 600 either might continue onto the process at block 620′ or mightcontinue onto the process at block 640 or block 645 in FIG. 6B,following the circular marker denoted, “A.” The processes at blocks 640and 645 may be as described in detail above, where after the process atblock 640 or at block 645, method 600 might continue onto the process atblock 620′ in FIG. 6E, following the circular marker denoted, “B.”

At block 620′ in FIG. 6E (either continuing from the process at block615′ or following the circular marker denoted, “B,” from the process atblock 640 or at block 645 in FIG. 6B), method 600 might comprisecomparing, with the first micro orchestrator, the first request-basedparsimony tree with one or more first network-based parsimony treesamong the plurality of first network-based parsimony trees to determinea fitness score for each first network-based parsimony tree. In someinstances, as in the embodiment of FIG. 6A, each fitness score might bea value indicative of a level of heuristic matching (in some cases,embodied as a percentage match) between the first request-basedparsimony tree with one of the one or more first network-based parsimonytrees. In some embodiments, comparing the first request-based parsimonytree with one or more first network-based parsimony trees might comprisecomparing the first request-based parsimony tree with one or more firstnetwork-based parsimony trees using one or more GPUs, or the like.

Method 600 might further comprise identifying, with the first microorchestrator, a best-fit network-based parsimony tree among the one ormore first network-based parsimony trees based on the fitness scores ofthe one or more first network-based parsimony trees (block 625′);identifying, with the first micro orchestrator, one or more firstnetwork resources among a first plurality of network resources forproviding the requested network services, based at least in part onnetwork resources represented within the identified best-fitnetwork-based parsimony tree (block 630′); and allocating, with thefirst micro orchestrator, at least one first network resource among theidentified one or more first network resources for providing therequested network services (block 635′). According to some embodiments,identifying the best-fit network-based parsimony tree might compriseidentifying the most parsimonious first network-based parsimony tree forproviding the requested network resources. That is, the first microorchestrator might identify the tree with the simplest (or leastcomplicated) network characteristics or the tree with the shortest (orfewest) network routing requirements, or the like, that enablesallocation of the requested network services with the desiredcharacteristics and performance parameters. In some embodiments, atleast one of generating first network-based parsimony trees, comparingthe first request-based parsimony tree with the one or more firstnetwork-based parsimony tree, identifying the best-fit network-basedparsimony tree, or identifying the one or more first network resourcesmay be performed using one or more of at least one ML system, at leastone AI systems, or at least one NN system, and/or the like.

Method 600 might continue onto the process at block 650 in FIG. 6C,following the circular marker denoted, “C.” The processes at blocks650-660 in FIG. 6C (following the circular marker denoted, “C”) may beas described in detail above, where after the process at block 660,method 600 might return to the process at block 615′ in FIG. 6E,following the circular marker denoted, “D.”

Exemplary System and Hardware Implementation

FIG. 7 is a block diagram illustrating an exemplary computer or systemhardware architecture, in accordance with various embodiments. FIG. 7provides a schematic illustration of one embodiment of a computer system700 of the service provider system hardware that can perform the methodsprovided by various other embodiments, as described herein, and/or canperform the functions of computer or hardware system (i.e., macroorchestrators 105 and 305, micro orchestrators 110, 325, and 350, userdevices 125 a-125 n and 310, domain managers 135 a, 135 b, 335 a, 335 b,360 a-360 c, network resources or devices or pools 140, 340 a-340 d, and365 a-365 d, audit engine 170, and computing system 505, etc.), asdescribed above. It should be noted that FIG. 7 is meant only to providea generalized illustration of various components, of which one or more(or none) of each may be utilized as appropriate. FIG. 7, therefore,broadly illustrates how individual system elements may be implemented ina relatively separated or relatively more integrated manner.

The computer or hardware system 700—which might represent an embodimentof the computer or hardware system (i.e., macro orchestrators 105 and305, micro orchestrators 110, 325, and 350, user devices 125 a-125 n and310, domain managers 135 a, 135 b, 335 a, 335 b, 360 a-360 c, networkresources or devices or pools 140, 340 a-340 d, 365 a-365 d, and auditengine 170, and computing system 505, etc.), described above withrespect to FIGS. 1-6—is shown comprising hardware elements that can beelectrically coupled via a bus 705 (or may otherwise be incommunication, as appropriate). The hardware elements may include one ormore processors 710, including, without limitation, one or moregeneral-purpose processors and/or one or more special-purpose processors(such as microprocessors, digital signal processing chips, graphicsacceleration processors, and/or the like); one or more input devices715, which can include, without limitation, a mouse, a keyboard, and/orthe like; and one or more output devices 720, which can include, withoutlimitation, a display device, a printer, and/or the like.

The computer or hardware system 700 may further include (and/or be incommunication with) one or more storage devices 725, which can comprise,without limitation, local and/or network accessible storage, and/or caninclude, without limitation, a disk drive, a drive array, an opticalstorage device, solid-state storage device such as a random accessmemory (“RAM”) and/or a read-only memory (“ROM”), which can beprogrammable, flash-updateable, and/or the like. Such storage devicesmay be configured to implement any appropriate data stores, including,without limitation, various file systems, database structures, and/orthe like.

The computer or hardware system 700 might also include a communicationssubsystem 730, which can include, without limitation, a modem, a networkcard (wireless or wired), an infra-red communication device, a wirelesscommunication device and/or chipset (such as a Bluetooth™ device, an802.11 device, a WiFi device, a WiMax device, a WWAN device, cellularcommunication facilities, etc.), and/or the like. The communicationssubsystem 730 may permit data to be exchanged with a network (such asthe network described below, to name one example), with other computeror hardware systems, and/or with any other devices described herein. Inmany embodiments, the computer or hardware system 700 will furthercomprise a working memory 735, which can include a RAM or ROM device, asdescribed above.

The computer or hardware system 700 also may comprise software elements,shown as being currently located within the working memory 735,including an operating system 740, device drivers, executable libraries,and/or other code, such as one or more application programs 745, whichmay comprise computer programs provided by various embodiments(including, without limitation, hypervisors, VMs, and the like), and/ormay be designed to implement methods, and/or configure systems, providedby other embodiments, as described herein. Merely by way of example, oneor more procedures described with respect to the method(s) discussedabove might be implemented as code and/or instructions executable by acomputer (and/or a processor within a computer); in an aspect, then,such code and/or instructions can be used to configure and/or adapt ageneral purpose computer (or other device) to perform one or moreoperations in accordance with the described methods.

A set of these instructions and/or code might be encoded and/or storedon a non-transitory computer readable storage medium, such as thestorage device(s) 725 described above. In some cases, the storage mediummight be incorporated within a computer system, such as the system 700.In other embodiments, the storage medium might be separate from acomputer system (i.e., a removable medium, such as a compact disc,etc.), and/or provided in an installation package, such that the storagemedium can be used to program, configure, and/or adapt a general purposecomputer with the instructions/code stored thereon. These instructionsmight take the form of executable code, which is executable by thecomputer or hardware system 700 and/or might take the form of sourceand/or installable code, which, upon compilation and/or installation onthe computer or hardware system 700 (e.g., using any of a variety ofgenerally available compilers, installation programs,compression/decompression utilities, etc.) then takes the form ofexecutable code.

It will be apparent to those skilled in the art that substantialvariations may be made in accordance with specific requirements. Forexample, customized hardware (such as programmable logic controllers,field-programmable gate arrays, application-specific integratedcircuits, and/or the like) might also be used, and/or particularelements might be implemented in hardware, software (including portablesoftware, such as applets, etc.), or both. Further, connection to othercomputing devices such as network input/output devices may be employed.

As mentioned above, in one aspect, some embodiments may employ acomputer or hardware system (such as the computer or hardware system700) to perform methods in accordance with various embodiments of theinvention. According to a set of embodiments, some or all of theprocedures of such methods are performed by the computer or hardwaresystem 700 in response to processor 710 executing one or more sequencesof one or more instructions (which might be incorporated into theoperating system 740 and/or other code, such as an application program745) contained in the working memory 735. Such instructions may be readinto the working memory 735 from another computer readable medium, suchas one or more of the storage device(s) 725. Merely by way of example,execution of the sequences of instructions contained in the workingmemory 735 might cause the processor(s) 710 to perform one or moreprocedures of the methods described herein.

The terms “machine readable medium” and “computer readable medium,” asused herein, refer to any medium that participates in providing datathat causes a machine to operate in a specific fashion. In an embodimentimplemented using the computer or hardware system 700, various computerreadable media might be involved in providing instructions/code toprocessor(s) 710 for execution and/or might be used to store and/orcarry such instructions/code (e.g., as signals). In manyimplementations, a computer readable medium is a non-transitory,physical, and/or tangible storage medium. In some embodiments, acomputer readable medium may take many forms, including, but not limitedto, non-volatile media, volatile media, or the like. Non-volatile mediaincludes, for example, optical and/or magnetic disks, such as thestorage device(s) 725. Volatile media includes, without limitation,dynamic memory, such as the working memory 735. In some alternativeembodiments, a computer readable medium may take the form oftransmission media, which includes, without limitation, coaxial cables,copper wire, and fiber optics, including the wires that comprise the bus705, as well as the various components of the communication subsystem730 (and/or the media by which the communications subsystem 730 providescommunication with other devices). In an alternative set of embodiments,transmission media can also take the form of waves (including withoutlimitation radio, acoustic, and/or light waves, such as those generatedduring radio-wave and infra-red data communications).

Common forms of physical and/or tangible computer readable mediainclude, for example, a floppy disk, a flexible disk, a hard disk,magnetic tape, or any other magnetic medium, a CD-ROM, any other opticalmedium, punch cards, paper tape, any other physical medium with patternsof holes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chipor cartridge, a carrier wave as described hereinafter, or any othermedium from which a computer can read instructions and/or code.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to the processor(s) 710for execution. Merely by way of example, the instructions may initiallybe carried on a magnetic disk and/or optical disc of a remote computer.A remote computer might load the instructions into its dynamic memoryand send the instructions as signals over a transmission medium to bereceived and/or executed by the computer or hardware system 700. Thesesignals, which might be in the form of electromagnetic signals, acousticsignals, optical signals, and/or the like, are all examples of carrierwaves on which instructions can be encoded, in accordance with variousembodiments of the invention.

The communications subsystem 730 (and/or components thereof) generallywill receive the signals, and the bus 705 then might carry the signals(and/or the data, instructions, etc. carried by the signals) to theworking memory 735, from which the processor(s) 705 retrieves andexecutes the instructions. The instructions received by the workingmemory 735 may optionally be stored on a storage device 725 eitherbefore or after execution by the processor(s) 710.

As noted above, a set of embodiments comprises methods and systems forimplementing network services orchestration, and, more particularly, tomethods, systems, and apparatuses for implementing intent-basedmulti-tiered orchestration and automation and/or implementingintent-based orchestration using network parsimony trees. FIG. 8illustrates a schematic diagram of a system 800 that can be used inaccordance with one set of embodiments. The system 800 can include oneor more user computers, user devices, or customer devices 805. A usercomputer, user device, or customer device 805 can be a general purposepersonal computer (including, merely by way of example, desktopcomputers, tablet computers, laptop computers, handheld computers, andthe like, running any appropriate operating system, several of which areavailable from vendors such as Apple, Microsoft Corp., and the like),cloud computing devices, a server(s), and/or a workstation computer(s)running any of a variety of commercially-available UNIX™ or UNIX-likeoperating systems. A user computer, user device, or customer device 805can also have any of a variety of applications, including one or moreapplications configured to perform methods provided by variousembodiments (as described above, for example), as well as one or moreoffice applications, database client and/or server applications, and/orweb browser applications. Alternatively, a user computer, user device,or customer device 805 can be any other electronic device, such as athin-client computer, Internet-enabled mobile telephone, and/or personaldigital assistant, capable of communicating via a network (e.g., thenetwork(s) 810 described below) and/or of displaying and navigating webpages or other types of electronic documents. Although the exemplarysystem 800 is shown with two user computers, user devices, or customerdevices 805, any number of user computers, user devices, or customerdevices can be supported.

Certain embodiments operate in a networked environment, which caninclude a network(s) 810. The network(s) 810 can be any type of networkfamiliar to those skilled in the art that can support datacommunications using any of a variety of commercially-available (and/orfree or proprietary) protocols, including, without limitation, TCP/IP,SNA™, IPX™, AppleTalk™, and the like. Merely by way of example, thenetwork(s) 810 (similar to network(s) 115, 130, 145 a-145 n, and/or 150a-150 n of FIG. 1, or the like) can each include a local area network(“LAN”), including, without limitation, a fiber network, an Ethernetnetwork, a Token-Ring™ network, and/or the like; a wide-area network(“WAN”); a wireless wide area network (“WWAN”); a virtual network, suchas a virtual private network (“VPN”); the Internet; an intranet; anextranet; a public switched telephone network (“PSTN”); an infra-rednetwork; a wireless network, including, without limitation, a networkoperating under any of the IEEE 802.11 suite of protocols, theBluetooth™ protocol known in the art, and/or any other wirelessprotocol; and/or any combination of these and/or other networks. In aparticular embodiment, the network might include an access network ofthe service provider (e.g., an Internet service provider (“ISP”)). Inanother embodiment, the network might include a core network of theservice provider, and/or the Internet.

Embodiments can also include one or more server computers 815. Each ofthe server computers 815 may be configured with an operating system,including, without limitation, any of those discussed above, as well asany commercially (or freely) available server operating systems. Each ofthe servers 815 may also be running one or more applications, which canbe configured to provide services to one or more clients 805 and/orother servers 815.

Merely by way of example, one of the servers 815 might be a data server,a web server, a cloud computing device(s), or the like, as describedabove. The data server might include (or be in communication with) a webserver, which can be used, merely by way of example, to process requestsfor web pages or other electronic documents from user computers 805. Theweb server can also run a variety of server applications, including HTTPservers, FTP servers, CGI servers, database servers, Java servers, andthe like. In some embodiments of the invention, the web server may beconfigured to serve web pages that can be operated within a web browseron one or more of the user computers 805 to perform methods of theinvention.

The server computers 815, in some embodiments, might include one or moreapplication servers, which can be configured with one or moreapplications accessible by a client running on one or more of the clientcomputers 805 and/or other servers 815. Merely by way of example, theserver(s) 815 can be one or more general purpose computers capable ofexecuting programs or scripts in response to the user computers 805and/or other servers 815, including, without limitation, webapplications (which might, in some cases, be configured to performmethods provided by various embodiments). Merely by way of example, aweb application can be implemented as one or more scripts or programswritten in any suitable programming language, such as Java™, C, C#™ orC++, and/or any scripting language, such as Perl, Python, or TCL, aswell as combinations of any programming and/or scripting languages. Theapplication server(s) can also include database servers, including,without limitation, those commercially available from Oracle™,Microsoft™, Sybase™, IBM™, and the like, which can process requests fromclients (including, depending on the configuration, dedicated databaseclients, API clients, web browsers, etc.) running on a user computer,user device, or customer device 805 and/or another server 815. In someembodiments, an application server can perform one or more of theprocesses for implementing network services orchestration, and, moreparticularly, to methods, systems, and apparatuses for implementingintent-based multi-tiered orchestration and automation and/orimplementing intent-based orchestration using network parsimony trees,as described in detail above. Data provided by an application server maybe formatted as one or more web pages (comprising HTML, JavaScript,etc., for example) and/or may be forwarded to a user computer 805 via aweb server (as described above, for example). Similarly, a web servermight receive web page requests and/or input data from a user computer805 and/or forward the web page requests and/or input data to anapplication server. In some cases, a web server may be integrated withan application server.

In accordance with further embodiments, one or more servers 815 canfunction as a file server and/or can include one or more of the files(e.g., application code, data files, etc.) necessary to implementvarious disclosed methods, incorporated by an application running on auser computer 805 and/or another server 815. Alternatively, as thoseskilled in the art will appreciate, a file server can include allnecessary files, allowing such an application to be invoked remotely bya user computer, user device, or customer device 805 and/or server 815.

It should be noted that the functions described with respect to variousservers herein (e.g., application server, database server, web server,file server, etc.) can be performed by a single server and/or aplurality of specialized servers, depending on implementation-specificneeds and parameters.

In certain embodiments, the system can include one or more databases 820a-820 n (collectively, “databases 820”). The location of each of thedatabases 820 is discretionary: merely by way of example, a database 820a might reside on a storage medium local to (and/or resident in) aserver 815 a (and/or a user computer, user device, or customer device805). Alternatively, a database 820 n can be remote from any or all ofthe computers 805, 815, so long as it can be in communication (e.g., viathe network 810) with one or more of these. In a particular set ofembodiments, a database 820 can reside in a storage-area network (“SAN”)familiar to those skilled in the art. (Likewise, any necessary files forperforming the functions attributed to the computers 805, 815 can bestored locally on the respective computer and/or remotely, asappropriate.) In one set of embodiments, the database 820 can be arelational database, such as an Oracle database, that is adapted tostore, update, and retrieve data in response to SQL-formatted commands.The database might be controlled and/or maintained by a database server,as described above, for example.

According to some embodiments, system 800 might further comprise macroorchestrator 825 (similar to macro orchestrators 105 and 305 of FIGS. 1and 3, or the like), one or more micro orchestrators 830 (similar tomicro orchestrators 110, 325, and 350 of FIGS. 1 and 3, or the like),one or more domain managers 835 (similar to domain managers 135 a, 135b, 335 a, 335 b, and 360 a-360 c of FIGS. 1 and 3, or the like), one ormore network resources 840 (similar to network resources or devices orpools 140, 340 a-340 d, and 365 a-365 d of FIGS. 1 and 3, or the like),quality of service (“QoS”) test and validate server or audit engine 845(similar to QoS test and validate server or audit engine 170 of FIG. 1,or the like), resource inventory database 850 (similar to resourceinventory database 155 of FIG. 1, or the like), intent metadata database855 (similar to intent metadata database 160 of FIG. 1, or the like),and active inventory database 860 (similar to active inventory database165 of FIG. 1, or the like). System 800 might further comprise computingsystem 865 (similar to computing system 505 of FIG. 5, of the like).

In operation, the macro orchestrator 825 might receive, over a network,a request for network services from a user device associated with acustomer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices, without information regarding any of specific hardware,specific hardware type, specific location, or specific network forproviding the requested network services. The macro orchestrator 825might send, to a first micro orchestrator among a plurality of microorchestrators (e.g., the one or more micro orchestrators 830, or thelike), the received request for network services, where the macroorchestrator automates, manages, or controls each of the plurality ofmicro orchestrators, while each micro orchestrator automates, manages,or controls at least one of a plurality of domain managers (e.g., theone or more domain managers 835, or the like) or a plurality of networkresources (e.g., network resources 840, or the like). In response toreceiving the request for network services, the first micro orchestratormight identify one or more first network resources among a firstplurality of network resources for providing the requested networkservices, based at least in part on the desired characteristics andperformance parameters, and based at least in part on a determinationthat the one or more network resources are capable of providing networkservices having the desired characteristics and performance parameters.The first micro orchestrator might allocate at least one first networkresource among the identified one or more first network resources forproviding the requested network services.

In some embodiments, the first micro orchestrator might (continually,occasionally, randomly, or in response to a request for data, or thelike) receive, from one or more first domain managers among a firstplurality of domain managers in communication with the first microorchestrator, data regarding the first plurality of network resourcesthat are automated, managed, or controlled by each of the one or morefirst domain managers. In such cases, identifying, with the first microorchestrator, one or more first network resources among a firstplurality of network resources for providing the requested networkservices might comprise identifying, with the first micro orchestrator,one or more first network resources among a first plurality of networkresources for providing the requested network services, based at leastin part on the data regarding the one or more first network resources,based at least in part on the desired characteristics and performanceparameters, and based at least in part on a determination that the oneor more network resources are capable of providing network serviceshaving the desired characteristics and performance parameters.

According to some embodiments, allocating, with the first microorchestrator, at least one first network resource among the identifiedone or more first network resources for providing the requested networkservices might comprise: sending, with the first micro orchestrator,commands to at least one first domain manager among the one or morefirst domain managers that automate, manage, or control the at least onefirst network resource; and in response to receiving the commands fromthe first micro orchestrator: determining, with the at least one firstdomain manager, an intent based at least in part on the desiredcharacteristics and performance parameters as comprised in the requestfor network services; generating and sending, with the at least onefirst domain manager, device language instructions for allocating the atleast one first network resource; and implementing, with the at leastone first domain manager, the at least one first network resource on theuser device associated with the customer, to provide the requestednetwork services.

In some embodiments, one of the macro orchestrator or the first microorchestrator might update a resource database (e.g., resource inventorydatabase 850, intent metadata database 855, active inventory database860, and/or data lake or database(s) 820 a-820 n, or the like) withinformation indicating that the at least one first network resource hasbeen allocated for providing the requested network services and withinformation indicative of the desired characteristics and performanceparameters as comprised in the request for network services. In somecases, an audit engine (e.g., audit engine 845, or the like) mightdetermine whether each of the identified one or more first networkresources conforms with the desired characteristics and performanceparameters. In some instances, determining whether each of theidentified one or more first network resources conforms with the desiredcharacteristics and performance parameters might comprise determining,with the audit engine, whether each of the identified one or more firstnetwork resources conforms with the desired characteristics andperformance parameters on a periodic basis or in response to a requestto perform an audit. Alternatively, or additionally, determining whethereach of the identified one or more first network resources conforms withthe desired characteristics and performance parameters might comprisedetermining, with the audit engine, whether each of the identified oneor more first network resources conforms with the desiredcharacteristics and performance parameters, by: measuring one or morenetwork performance metrics of each of the identified one or more firstnetwork resources; comparing, with the audit engine, the measured one ormore network performance metrics of each of the identified one or morefirst network resources with the desired performance parameters;determining characteristics of each of the identified one or more firstnetwork resources; and comparing, with the audit engine, the determinedcharacteristics of each of the identified one or more first networkresources with the desired characteristics.

In such cases, each of the one or more network performance metrics mightinclude, without limitation, at least one of quality of service (“QoS”)measurement data, platform resource data and metrics, service usagedata, topology and reference data, historical network data, networkusage trend data, or one or more of information regarding at least oneof latency, jitter, bandwidth, packet loss, nodal connectivity, computeresources, storage resources, memory capacity, routing, operationssupport systems (“OSS”), or business support systems (“BSS”) orinformation regarding at least one of fault, configuration, accounting,performance, or security (“FCAPS”), and/or the like.

According to some embodiments, based on a determination that at leastone identified network resource among the identified one or more firstnetwork resources fails to conform with the desired performanceparameters within first predetermined thresholds or based on adetermination that the determined characteristics of the at least oneidentified network resource fails to conform with the desiredcharacteristics within second predetermined thresholds, the first microorchestrator either might reconfigure the at least one identifiednetwork resource to provide the desired characteristics and performanceparameters; or might reallocate at least one other identified networkresources among the identified one or more first network resources forproviding the requested network services.

In some aspects, one or more parsimony trees might be generated, basedon network telemetry data of one or more networks, where each parsimonytree might be a graphical representation of characteristics andperformance parameters based on the network telemetry data of the one ormore networks, and the system might perform network orchestration andautomation based on the generated one or more parsimony trees. Inparticular, the macro orchestrator 825 and/or computing system 865(similar to computing system 505 of FIG. 5, or the like) might receive,over a network, a request for network services from a user deviceassociated with a customer, the request for network services comprisingdesired characteristics and performance parameters for the requestednetwork services, without information regarding any of specifichardware, specific hardware type, specific location, or specific networkfor providing the requested network services. The macro orchestrator 825and/or the computing system 865 might send, to a first microorchestrator among a plurality of micro orchestrators (e.g., the one ormore micro orchestrators 830, or the like), the received request fornetwork services, where the macro orchestrator automates, manages, orcontrols each of the plurality of micro orchestrators, while each microorchestrator automates, manages, or controls at least one of a pluralityof domain managers (e.g., the one or more domain managers 835, or thelike) or a plurality of network resources (e.g., network resources 840,or the like). In response to receiving the request for network services,the first micro orchestrator and/or the computing system 865 mightgenerate a first request-based parsimony tree based at least in part onthe desired characteristics and performance parameters contained in therequest for network services.

According to some embodiments, the first request-based parsimony treemight be a graphical representation including, without limitation, anend-point of a first portion representing delivery location of therequested network services, an endpoint of each of one or more secondportions that connect with the first portion representing a serviceprovider site, each intersection between two or more second portions orbetween the first portion and one of the second portions representing anetwork resource node, and characteristics of the first and secondportions representing the desired characteristics and performanceparameters contained in the request for network services, and/or thelike. In some cases, the plurality of micro orchestrators might eachinclude, but is not limited to, one of a server computer over a network,a cloud-based computing system over a network, or a distributedcomputing system, and/or the like.

The first micro orchestrator and/or the computing system 865 mightaccess, from a datastore, a plurality of first network-based parsimonytrees, each of the plurality of first network-based parsimony treesbeing generated based on measured network metrics. In some embodiments,each first network-based parsimony tree might be a graphicalrepresentation including, but not limited to, an end-point of a thirdportion representing the delivery location of the requested networkservices, an endpoint of each of one or more fourth portions thatconnect with the third portion representing a service provider site,each intersection between two or more fourth portions or between thethird portion and one of the fourth portions representing a networkresource node, and characteristics of the third and fourth portionsrepresenting measured characteristics and performance parameters basedon the measured network metrics.

According to some embodiments, the first portion of the firstrequest-based parsimony tree and the third portion of each firstnetwork-based parsimony tree might each be represented by a trunk, whilethe one or more second portions of the first request-based parsimonytree and the one or more fourth portions of each first network-basedparsimony tree might each be represented by a branch, and, in eachparsimony tree, one or more branches might connect with the trunk. Insome cases, in each of at least one parsimony tree, two or more branchesmight connect with each other via one or more connector branches and viathe trunk, or the like. In some instances, each portion of each firstnetwork-based parsimony tree might be represented by a secondnetwork-based parsimony tree among a plurality of second network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion, where each portion of each secondnetwork-based parsimony tree might be represented by a thirdnetwork-based parsimony tree among a plurality of third network-basedparsimony tree that is indicative of characteristics and performanceparameters of that portion, and so on in a fractal-like manner.

In some embodiments, the characteristics of the first and secondportions and the characteristics of the third and fourth portions mightinclude, without limitation, at least one of thickness of each portion,length of each portion from the delivery location of the requestednetwork services, number of network resource nodes on each portion,color-code of each portion, number of second or fourth portions, angleof each second or fourth portion relative to the first or third portion,number of any connector portions between two or more second or fourthportions, relative location of any connector portions between two ormore second or fourth portions, length of any connector portions betweentwo or more second or fourth portions, or angle of any connectorportions between two or more second or fourth portions, and/or the like.In some instances, the characteristics of the first and second portionsand the characteristics of the third and fourth portions might representone or more of latency, jitter, packet loss, number of hops, bandwidth,utilization, capacity, or proximity, and/or the like.

According to some embodiments, the first micro orchestrator and/or thecomputing system 865 might compare the first request-based parsimonytree with one or more first network-based parsimony trees among theplurality of first network-based parsimony trees to determine a fitnessscore for each first network-based parsimony tree. In some instances,each fitness score might be a value indicative of a level of heuristicmatching (in some cases, embodied as a percentage match) between thefirst request-based parsimony tree with one of the one or more firstnetwork-based parsimony trees. In some embodiments, comparing the firstrequest-based parsimony tree with one or more first network-basedparsimony trees might comprise comparing the first request-basedparsimony tree with one or more first network-based parsimony treesusing one or more GPUs, or the like.

Merely by way of example, in some cases, the first micro orchestratorand/or the computing system 865 might identify a best-fit network-basedparsimony tree among the one or more first network-based parsimony treesbased on the fitness scores of the one or more first network-basedparsimony trees; might identify one or more first network resourcesamong a first plurality of network resources for providing the requestednetwork services, based at least in part on network resourcesrepresented within the identified best-fit network-based parsimony tree;and might allocate at least one first network resource among theidentified one or more first network resources for providing therequested network services. According to some embodiments, identifyingthe best-fit network-based parsimony tree might comprise identifying themost parsimonious first network-based parsimony tree for providing therequested network resources. That is, the first micro orchestratorand/or the computing system 865 might identify the tree with thesimplest (or least complicated) network characteristics or the tree withthe shortest (or fewest) network routing requirements, or the like, thatenables allocation of the requested network services with the desiredcharacteristics and performance parameters. In some embodiments, atleast one of generating first network-based parsimony trees, comparingthe first request-based parsimony tree with the one or more firstnetwork-based parsimony tree, identifying the best-fit network-basedparsimony tree, or identifying the one or more first network resourcesmay be performed using one or more of at least one ML system, at leastone AI systems, or at least one NN system, and/or the like.

In some embodiments, the first micro orchestrator and/or the computingsystem 865 might apply a first filter to at least one firstnetwork-based parsimony tree among the one or more first network-basedparsimony trees to filter out one or more characteristics or one or moresub-characteristics, prior to comparing the first request-basedparsimony tree with the one or more first network-based parsimony trees.According to some embodiments, the characteristics of the third andfourth portions might include color-codes embodied as a colortemperature or range of colors for each portion or for each parsimonytree that is indicative of characteristics or performance parametersincluding one or more of latency, jitter, packet loss, number of hops,bandwidth, utilization, capacity, or proximity, and/or the like. In suchcases, alternative or additional to applying the first filter, the firstmicro orchestrator and/or the computing system 865 might apply a secondfilter to at least one first network-based parsimony tree among the oneor more first network-based parsimony trees to change the colortemperature based on changes in measured network metrics.

According to some embodiments, the first micro orchestrator and/or thecomputing system 865 might receive updated measured network metrics;might, in response to receiving the updated measured network metrics,generate a plurality of updated first network-based parsimony trees; andmight replace the plurality of first network-based parsimony trees inthe datastore with the plurality of updated first network-basedparsimony trees. In some embodiments, the updated measured networkmetrics might be received according to one of the following: on aperiodic basis, on a continual basis, on a random basis, or in responseto a change in network characteristic or performance in at least onenetwork resource in a network, and/or the like. In some cases, each ofthe plurality of updated first network-based parsimony trees might bestored in the datastore as an image file (e.g., .jpg file, .tiff file,.gif file, .bmp file, .png file, .dwf file, .dwg file, .drw file, .stlfile, .pdf file, .svg file, .cgm file, etc.).

In some embodiments, rather than a single request-based parsimony treebeing generated in response to receiving the request for networkservices, the first micro orchestrator and/or the computing system 865might generate a plurality of first request-based parsimony trees, eachrepresenting a desired characteristic or performance parameter, and thesubsequent functions performed by the first orchestrator and/or thecomputing system 865 might be performed on this plurality of firstrequest-based parsimony trees rather than the single request-basedparsimony tree.

These and other functions of the system 800 (and its components) aredescribed in greater detail above with respect to FIGS. 1-6.

While certain features and aspects have been described with respect toexemplary embodiments, one skilled in the art will recognize thatnumerous modifications are possible. For example, the methods andprocesses described herein may be implemented using hardware components,software components, and/or any combination thereof. Further, whilevarious methods and processes described herein may be described withrespect to particular structural and/or functional components for easeof description, methods provided by various embodiments are not limitedto any particular structural and/or functional architecture but insteadcan be implemented on any suitable hardware, firmware and/or softwareconfiguration. Similarly, while certain functionality is ascribed tocertain system components, unless the context dictates otherwise, thisfunctionality can be distributed among various other system componentsin accordance with the several embodiments.

Moreover, while the procedures of the methods and processes describedherein are described in a particular order for ease of description,unless the context dictates otherwise, various procedures may bereordered, added, and/or omitted in accordance with various embodiments.Moreover, the procedures described with respect to one method or processmay be incorporated within other described methods or processes;likewise, system components described according to a particularstructural architecture and/or with respect to one system may beorganized in alternative structural architectures and/or incorporatedwithin other described systems. Hence, while various embodiments aredescribed with—or without—certain features for ease of description andto illustrate exemplary aspects of those embodiments, the variouscomponents and/or features described herein with respect to a particularembodiment can be substituted, added and/or subtracted from among otherdescribed embodiments, unless the context dictates otherwise.Consequently, although several exemplary embodiments are describedabove, it will be appreciated that the invention is intended to coverall modifications and equivalents within the scope of the followingclaims.

What is claimed is:
 1. A method, comprising: receiving, with a computingsystem, a request for network services from a user device associatedwith a customer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices; in response to receiving the request for network services,generating, with the computing system, a first request-based parsimonytree based at least in part on the desired characteristics andperformance parameters contained in the request for network services,the first request-based parsimony tree being a graphical representationcomprising an end-point of a first portion representing deliverylocation of the requested network services, an endpoint of each of oneor more second portions that connect with the first portion representinga service provider site; accessing, with the computing system and from adatastore, a plurality of first network-based parsimony trees, each ofthe plurality of first network-based parsimony trees being generatedbased on measured network metrics, each first network-based parsimonytree being a graphical representation comprising an end-point of a thirdportion representing the delivery location of the requested networkservices, an endpoint of each of one or more fourth portions thatconnect with the third portion representing a service provider site;comparing, with the computing system, the first request-based parsimonytree with one or more first network-based parsimony trees among theplurality of first network-based parsimony trees to determine a fitnessscore for each first network-based parsimony tree, each fitness scorebeing a value indicative of a level of heuristic matching between thefirst request-based parsimony tree with one of the one or more firstnetwork-based parsimony trees; identifying, with the computing system, abest-fit network-based parsimony tree among the one or more firstnetwork-based parsimony trees based on the fitness scores of the one ormore first network-based parsimony trees; identifying, with thecomputing system, one or more first network resources among a firstplurality of network resources for providing the requested networkservices, based at least in part on network resources represented withinthe identified best-fit network-based parsimony tree; and allocating,with the computing system, at least one first network resource among theidentified one or more first network resources for providing therequested network services.
 2. The method of claim 1, wherein thecomputing system comprises one of a server computer over a network, oneor more graphics processing units (“GPUs”), a cloud-based computingsystem over a network, or a distributed computing system.
 3. The methodof claim 1, wherein the desired performance parameters comprise at leastone of a maximum latency, a maximum jitter, a maximum packet loss, amaximum cost, or a maximum number of hops.
 4. The method of claim 1,wherein the desired characteristics comprise at least one of requirementfor network equipment to be geophysically proximate to the user deviceassociated with the customer, requirement for network equipment to belocated within a first geophysical location, requirement to avoidrouting network traffic through a second geophysical location,requirement to route network traffic through a third geophysicallocation, requirement to exclude a first type of network resources fromfulfillment of the requested network services, requirement to include asecond type of network resources for fulfillment of the requestednetwork services, requirement to fulfill the requested network servicesbased on a single goal indicated by the customer, or requirement tofulfill the requested network services based on multiple goals indicatedby the customer.
 5. The method of claim 1, wherein the first portion ofthe first request-based parsimony tree and the third portion of eachfirst network-based parsimony tree are each represented by a trunk,wherein the one or more second portions of the first request-basedparsimony tree and the one or more fourth portions of each firstnetwork-based parsimony tree are each represented by a branch, andwherein, in each parsimony tree, one or more branches connect with thetrunk.
 6. The method of claim 5, wherein, in each of at least oneparsimony tree, two or more branches connect with each other via one ormore connector branches and via the trunk.
 7. The method of claim 1,wherein the characteristics of the first and second portions and thecharacteristics of the third and fourth portions comprise at least oneof thickness of each portion, length of each portion from the deliverylocation of the requested network services, number of network resourcenodes on each portion, color-code of each portion, number of second orfourth portions, angle of each second or fourth portion relative to thefirst or third portion, number of any connector portions between two ormore second or fourth portions, relative location of any connectorportions between two or more second or fourth portions, length of anyconnector portions between two or more second or fourth portions, orangle of any connector portions between two or more second or fourthportions.
 8. The method of claim 7, wherein the characteristics of thefirst and second portions and the characteristics of the third andfourth portions represent one or more of latency, jitter, packet loss,number of hops, bandwidth, utilization, capacity, or proximity.
 9. Themethod of claim 7, further comprising: applying, with the computingsystem, a first filter to at least one first network-based parsimonytree among the one or more first network-based parsimony trees to filterout one or more characteristics or one or more sub-characteristics,prior to comparing the first request-based parsimony tree with the oneor more first network-based parsimony trees.
 10. The method of claim 7,wherein the characteristics of the third and fourth portions comprisecolor-codes embodied as a color temperature or range of colors for eachportion or for each parsimony tree that is indicative of characteristicsor performance parameters including one or more of latency, jitter,packet loss, number of hops, bandwidth, utilization, capacity, orproximity, wherein the method further comprises: applying, with thecomputing system, a second filter to at least one first network-basedparsimony tree among the one or more first network-based parsimony treesto change the color temperature based on changes in measured networkmetrics.
 11. The method of claim 1, wherein generating the firstrequest-based parsimony tree comprises generating a plurality of firstrequest-based parsimony trees, each representing a desiredcharacteristic or performance parameter; wherein the plurality of firstnetwork-based parsimony trees comprises a plurality of firstnetwork-based parsimony trees corresponding to each of the desiredcharacteristics and performance parameters, each of the plurality offirst network-based parsimony trees being generated based on measurednetwork metrics; wherein comparing the first request-based parsimonytree with one or more first network-based parsimony trees among theplurality of first network-based parsimony trees comprises comparing,with the computing system, each first request-based parsimony treerepresenting one of the desired characteristics and performanceparameters with a corresponding plurality of first network-basedparsimony trees; wherein identifying the best-fit network-basedparsimony tree comprises identifying, with the computing system, thebest-fit network-based parsimony tree corresponding to each of thedesired characteristics and performance parameters; and whereinidentifying the one or more first network resources is based at least inpart on the network resources represented within the identified best-fitnetwork-based parsimony trees.
 12. The method of claim 1, whereincomparing the first request-based parsimony tree with one or more firstnetwork-based parsimony trees comprises comparing the firstrequest-based parsimony tree with one or more first network-basedparsimony trees using one or more graphics processing units (“GPUs”).13. The method of claim 1, wherein each portion of each firstnetwork-based parsimony tree is represented by a second network-basedparsimony tree among a plurality of second network-based parsimony treethat is indicative of characteristics and performance parameters of thatportion, wherein each portion of each second network-based parsimonytree is represented by a third network-based parsimony tree among aplurality of third network-based parsimony tree that is indicative ofcharacteristics and performance parameters of that portion.
 14. Themethod of claim 1, further comprising: receiving, with the computingsystem, updated measured network metrics; and in response to receivingthe updated measured network metrics, generating, with the computingsystem, a plurality of updated first network-based parsimony trees, andreplacing, with the computing system, the plurality of firstnetwork-based parsimony trees in the datastore with the plurality ofupdated first network-based parsimony trees.
 15. The method of claim 14,wherein the updated measured network metrics are received according toone of the following: on a periodic basis, on a continual basis, on arandom basis, or in response to a change in network characteristic orperformance in at least one network resource in a network; and whereineach of the plurality of updated first network-based parsimony trees isstored in the datastore as an image file.
 16. The method of claim 1,wherein identifying the best-fit network-based parsimony tree comprisesidentifying the most parsimonious first network-based parsimony tree forproviding the requested network resources.
 17. The method of claim 1,wherein at least one of generating first network-based parsimony trees,comparing the first request-based parsimony tree with the one or morefirst network-based parsimony tree, identifying the best-fitnetwork-based parsimony tree, or identifying the one or more firstnetwork resources is performed using one or more of at least one machinelearning (“ML”) system, at least one artificial intelligence (“AI”)systems, or at least one neural network (“NN”) system.
 18. The method ofclaim 1, wherein receiving the request for network services from theuser device associated with the customer comprises receiving, with amacro orchestrator over a network, a request for network services from auser device associated with a customer; wherein generating the firstrequest-based parsimony tree comprises generating, with a first microorchestrator among a plurality of micro orchestrators, a firstrequest-based parsimony tree; wherein accessing the plurality of firstnetwork-based parsimony trees from the datastore comprises accessing,with the first micro orchestrator and from the datastore, a plurality offirst network-based parsimony trees; wherein comparing the firstrequest-based parsimony tree with the one or more first network-basedparsimony trees comprises comparing, with the first micro orchestrator,the first request-based parsimony tree with one or more firstnetwork-based parsimony trees; wherein identifying the best-fitnetwork-based parsimony tree among the one or more first network-basedparsimony trees comprises identifying, with the first microorchestrator, a best-fit network-based parsimony tree among the one ormore first network-based parsimony trees; wherein identifying the one ormore first network resources for providing the requested networkservices comprises identifying, with the first micro orchestrator, oneor more first network resources among a first plurality of networkresources for providing the requested network services; and whereinallocating the at least one first network resource among the identifiedone or more first network resources for providing the requested networkservices comprises allocating, with the first micro orchestrator, atleast one first network resource among the identified one or more firstnetwork resources for providing the requested network services.
 19. Asystem, comprising: a computing system, comprising: at least one firstprocessor; and a first non-transitory computer readable mediumcommunicatively coupled to the at least one first processor, the firstnon-transitory computer readable medium having stored thereon computersoftware comprising a first set of instructions that, when executed bythe at least one first processor, causes the computing system to:receive a request for network services from a user device associatedwith a customer, the request for network services comprising desiredcharacteristics and performance parameters for the requested networkservices; in response to receiving the request for network services,generate a first request-based parsimony tree based at least in part onthe desired characteristics and performance parameters contained in therequest for network services, the first request-based parsimony treebeing a graphical representation comprising an end-point of a firstportion representing delivery location of the requested networkservices, an endpoint of each of one or more second portions thatconnect with the first portion representing a service provider site;access, from a datastore, a plurality of first network-based parsimonytrees, each of the plurality of first network-based parsimony treesbeing generated based on measured network metrics, each firstnetwork-based parsimony tree being a graphical representation comprisingan end-point of a third portion representing the delivery location ofthe requested network services, an endpoint of each of one or morefourth portions that connect with the third portion representing aservice provider site; compare the first request-based parsimony treewith one or more first network-based parsimony trees among the pluralityof first network-based parsimony trees to determine a fitness score foreach first network-based parsimony tree, each fitness score being avalue indicative of a level of heuristic matching between the firstrequest-based parsimony tree with one of the one or more firstnetwork-based parsimony trees; identify a best-fit network-basedparsimony tree among the one or more first network-based parsimony treesbased on the fitness scores of the one or more first network-basedparsimony trees; identify one or more first network resources among afirst plurality of network resources for providing the requested networkservices, based at least in part on network resources represented withinthe identified best-fit network-based parsimony tree; and allocate atleast one first network resource among the identified one or more firstnetwork resources for providing the requested network services.
 20. Thesystem of claim 19, wherein the computing system comprises one of aserver computer over a network, one or more graphics processing units(“GPUs”), a cloud-based computing system over a network, or adistributed computing system.